In this context , we choose artificial neural network and make it much more tolerant to anomalies in the recorded image or data . Common optical character recognition tasks involve identifying simple edge detection and matching them with predefined patterns . In this research , characters are recognized even when noise such as inclination and skewedness presents , by training the network to look for discrepancies in data and relate them using vocabulary , grammar and common recurrences that may occur after a character . Many researchers have been developed the methods for recognising characters in by using Optical Character Recognition methods . Although text recognition problem using Optical Character Recognition has been more or less solved , most of the Optical Character Recognition problem explored is belong to Latin alphabet texts . Meanwhile , there are several languages have non-Latin scripts as the written text . In an optical character recognition problem , we compare ( as a function of training set size ) the performance of three neural network based ensemble methods ( two versions of boosting and a committee of neural networks trained independently ) to that of a single network . In boosting , the number of patterns actually used for training is a subset of all potential training patterns . A script independent character segmentation from word images technique has been reported here . Word to character segmentation is an important preprocessing step of optical character recognition process . But in case of handwritten text , presence of touching characters decreases the accuracy of the technique of the segmentation of the characters from the word . Unfortunately , contemporary methods of digitization and character recognition are not sufficient enough . During optical character recognition process , low quality of ancient manuscripts and on the other hand not enough advanced software result in numerous errors in output texts . The paper presents the results of the tests with ancient manuscripts in Polish , Latin , and English languages . With the development of optical character recognition research , many techniques for printed character recognition have been developed . But the character recognition study for the New Tai Lue has lagged behind . This paper presents a new comprehensive database for isolated offline handwritten Farsi/Arabic numbers and characters for use in optical character recognition research . The database is freely available for academic use . Deep neural networks help solving different images related tasks very efficiently , though their cost is high whereas a lot of data are required for training . While there is a great demand to build neural network models for optical character detection and recognition for different languages , such as , for mobile real-time applications , datasets collecting and labeling are quite expensive . In this paper , we propose the fully automated approach for synthetic images with text generation based on deep learning and projective geometry methods . In recent years , Optical Character Recognition(OCR ) is widely used in machine vision . In this paper , we investigated the problem of optical character detection and recognition for Image-based in natural scene . The Optical Character Recognition is divided into three steps : ( 1 ) Selecting the candidate regions through image preprocessing . This provides the theoretical foundation for performance extrapolation based on pseudolikelihood , unbiased estimation , and high-dimensional asymptotics . We investigate the robustness of our methods to non-generative classifiers in simulations and one optical character recognition example . The invention provides a device and a method for optical character recognition of images . The method includes : finding out an edge contour of a target character from a to-be-processed image ; during binarization processing of the target character , respectively taking each pixel point in the edge contour of the target character as a current pixel point to judge whether the current pixel point belongs to the target character or a background image of the target character ; if the current pixel point belongs to the target character , subjecting the current pixel point to binarization processing ; subjecting the image to character recognition after binarization processing . It works for different types of degraded document images . Then an artificial neural network is used for optical character recognition purpose . Degraded historical document images face many challenges in the process of optical character recognizing or word spotting , even after applying the traditional binarization techniques . In this paper , we propose a K-means based clustering technique for adaptive binarization of degraded document images . Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition . In this paper , Off-line Handwritten English Character recognition is done using Artificial Neural Network ( ANN ) . This paper is aimed at clarifying the role of handwriting recognition in accordance with today 's maturing technologies . It tries to list and clarify the components that build handwriting recognition and related technologies such as OCR ( Optical Character Recognition ) and Signature Veriication . This paper could also be regarded as a survey of handwriting recognition and related topics with a rich list of references for the interested reader . This paper presents a robust approach to extracting and summarizing the textual content of instructional videos for handwritten recognition , indexing and retrieval , and other e-learning applications . Content extraction from instructional videos is challenging due to image noise , light condition changes , camera movements , and unavoidable occlusions by instructors . Thus , one can for example , describe the position of intervals where a curve or a path is decreasing to the right or increasing to the right compared to a reference . And therefore , one returns to the type of description made in handwritten recognition where one has described relatively the respective position of intervals end numbers in a predefined order . In a handwriting recognition problem , characters can be represented using chain codes . The main problem in representing characters using chain code is optimizing the length of the chain code . Stability and style-variation are important characteristics in handwriting analysis and recognition . This paper describes a stability modelling technique of handwritings in the context of on-line signature verification . Researchers in the fields of document processing and handwriting analysis and recognition increasingly need to construct elaborate software systems to investigate and evaluate new research components such as cursive handwriting algorithms , contextual recognition systems and document parsing tools . Although a number of commercial and public-domain image processing frameworks already exist , for example Khoros , KBVision and the Image Understanding Environment ( IUE ) none of these systems fully supports the cycle of research , software development , informal and formal evaluation which is typically required by the research community . Digitization though is only the first step to make the information contained in manuscripts accessible to researchers and to the interested public . What we create after digitization is only a “ digital image ” of the page and further processing steps need to be applied during the handwriting recognition process , so that the manuscript ’s content is transformed into a form that is interpretable by a computer . One important step in the handwriting recognition process is that of text line extraction , which aims at extracting individual text lines from the manuscript page . It is difficult to identify the Arabic numerals'handwriting , especially the feature extraction and the handwriting characteristics recognition . The classification of features of traditional Arabic numerals handwriting are set up according to Chinese handwriting recognition feature classification , rather than the characteristics of the Arabic numerals . Consequently , any sorting algorithm , coupled with such a neural comparator , can sort any set of items . Although applications include ink segmentation ( for handwriting recognition purposes ) and Web page ranking , our emphasis on this paper is on classifier aggregation and , in particular , the integration of our standard handwriting recognizers with a user personalization database that consists of user samples Handwritten digits recognition is a well-researched sub area within the field of handwritten writing recognition , which is concerned with learning models to distinguish presegmented handwritten digits . However , handwritten digit recognition is still a difficult task because of the high variability in the digit shapes written by individuals . The Another majorareaofapplihation isthefaerecognition ( 24 , larger training datasetsareabigboontothese systems asthe 261an other biometrics . TheHandwriting recognition ( 7,11 performhance getsbetter andbetter withtheincrease indatasets . also Thehigher training datasetalthough drastically increases the . [ reference ] has shown Convolutional Neural Networks can perform well like MRFs / CRFs approaches in image restoration application . Another domain which benefits from the combination of CNNs and structured loss is handwriting recognition . In natural language processing , Yao et al . paragraph : Connection to multidimensional RNNs . Multidimensional RNNs ( MDRNN ) are an extension of 1D sequential RNNs that can accept multidimensional input e.g. images , and have been successfully applied to image segmentation and handwriting recognition . Notably multidimensional LSTMs ( MDLSTM ) have an analogous formulation to ours except the term and the fact that we use distinct weights per column ( or ‘ layer ’ in our case ) . Another advantage of ANNs often used to justify their use is the ease of hardware implementation ; however , in most publications , this did not seem to be the reason for application . The experiment on supervised classification , in handwritten digit recognition , showed that ANNs are quite capable of solving difficult object recognition problems . A number of ANN architectures were trained to mimic the Kuwahara filter , a nonlinear edge-preserving smoothing filter used in preprocessing . We also present a distance function that is used to measure the distance between an instance and a prototype . The new methods are compared with some of the best-known reduction techniques on the handwritten digit recognition problem . The results show that the techniques presented provide the best combination of amount of reduction and classification accuracy for both Arabic and Hindu digits . Convolutional Neural Network ( CNN ) , a popular machine learning algorithm , has been proven as a highly accurate and effective algorithm that has been used in a variety of applications such as handwriting digit recognition , visual recognition , and image classification . State-of-the-art CNNs are computationally intensive , yet their parallel and modular nature make platforms like Field Programmable Gate Arrays ( FPGAs ) well suited for the acceleration process . Compared with the previous works where Mahalanobis distance metric and instance weights are learned by using the labelled data or in a pipelined framework that leads to a decrease in the performance , our proposed SSMT attempts to learn a regularized distance metric and instance weights by considering unlabelled data in a parallel framework . Experimental evaluation on three cross-domain visual data sets , e.g. , PIE Face , Handwriting Digit Recognition on MNIST – USPS and Object Recognition , demonstrates the effectiveness of our designed approach on facilitating the unlabelled target task learning , compared to current state-of-the-art domain adaptation approaches . Moreover , it allows to incorporate a priori and application-driven knowledge into the basis vectors , during the learning process . We apply our dimensionality reduction method to images and compare it with principal component analysis ( PCA ) and non-negative matrix factorization ( NMF ) and its variants , in the context of handwritten digit image recognition and face recognition . The experimental results suggest that the proposed dimensionality reduction algorithm is competitive to PCA and NMF and that it results into meaningful features with high discriminant value . A solution using neural network to achieve preconditioned selection is suggested . Two examples on handprinted digit recognition are used to illustrate the potential gain . A modular and structured approach was taken in order to simplify network construction , optimization and analysis . Results on the task of handprinted digit recognition are among the best report to date on a set of real-world ZIP code digit images , provided by the United States Postal Service . The system achieved a 99.1 % recognition rate on the training set and a 96.0 % recognition rate on the test set with no rejections . After cutting the stem then performed feature extraction to obtain a vector which was fed to the neural network system for recognition rate . System back propagation neural network for handwritten digit pattern recognition was designed by 168neuron consists of input layer , 70 neurons in the hidden layer and 10 neurons in the output layer . The results obtained in this study were 83.78 % of the data slip can be recognized correctly . A function quantifying suitability of a selected subset is proposed , and a greedy algorithm for solving the subset selection problem is introduced . The algorithm is evaluated on hand digit recognition and other binary classification tasks , and its performance is compared to stratified sampling methods . We investigate the origin of the significantly different error rates between handwritten digit machine recognition and vowel sound machine recognition . While the error rate for five Korean vowel sounds , [ ɑ ] , [ ʊ ] , [ ɪ ] , [ ο ] , and [ ɛ ] , is about 10 percent , that of handwritten digit recognition is less than 1 percent for convolutional neural networks ( CNNs ) with raw data . Convolutional neural networks have gained much recent attention due to their success in image classification . Convnets trained with backpropagation were initially succesful in digit recognition and OCR . The feature representations learned from large data sets have been found to generalize well to other image classification tasks and even to object detection . subsection : Related work A first attempt to remove from GRUs has recently led to a single - gate architecture called Minimal Gated Recurrent Unit ( M - GRU ) , that achieves a performance comparable to that obtained by standard GRUs in handwritten digit recognition as well as in a sentiment classification task . To the best of our knowledge , our contribution is the first attempt that explores this architectural variation in speech recognition . A word spotting understanding server was implemented within the GALAXY [ 4 ] architecture using the JUPITER [ 3 ] weather information domain . Word spotting was implemented using a simple context-free grammar that maps words to key-value classes . Word spotting was evaluated by comparing understanding error rates against those of the TINA [ 17 ] natural language understanding component . word spotting is a technique which can extract the text from input image . This paper addresses the problems of word spotting and word recognition on images . . In recognition , the goal is to recognize the content of the word image , usually aided by a dictionary or lexicon . We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying . More precisely , a model is trained “ on the fly ” with images rendered from the searched words in one or multiple computer fonts . In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task . The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into ( i ) a codebook of shapes and ( ii ) a set of word-specific parameters . However , in spite of the great progress achieved , handwritten document word spotting still can hardly achieve acceptable performance on real-world handwritten document images that vary widely in writing style and quality . This paper gives an overview of published research efforts in the area of handwritten document image word spotting and on the technologies used in the field . We first start by describing a general model for document word spotting followed by discussing present challenges in handwritten document word spotting . Then the used databases for handwritten document word spotting and other handwritten text tasks are discussed . After that , research works on handwritten document word spotting are presented . We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains . We then train the model concurrently on textual sentiment analysis , speech recognition , image classification , action recognition from video , and handwriting word spotting of two different scripts ( Arabic and English ) . The model we propose successfully learned different tasks across multiple domains . Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes . The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples ; word-retrieval is therefore very difficult in low-resource scripts . Recommendations are also provided for future research . It is also observed that the research in this area is quietly thin and still more research is to be done , particularly in the case of word recognition of printed/handwritten documents in Indic scripts . In this paper , we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text . Image moments provide robust estimates for text characteristics such as size and position of words within an image . The secondary ( or artistic ) features are the parts of a character which contribute to its various representations . The reduced character methodology presented in this paper attempts to prove that the recognition of a reduced size character provides a robust approach for recognition of handwritten text . The RNN approach for handwritten character recognition is based upon recurrent neural networks . Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results . Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition . Many existing approaches incorporate sophisticated network structures to handle various shapes , use extra annotations for stronger supervision , or employ hard-to-train recurrent neural networks for sequence modeling . In this work , we propose a simple yet robust approach for irregular text recognition . With no need to convert input images to sequence representations , we directly connect two-dimensional CNN features to an attention-based sequence decoder . All six research papers achieve the significant progresses in their corresponding vision tasks . ( 1 ) In “ Progressive rectification network for irregular text recognition ” , Gao et al. propose a progressive rectification network ( PRN ) for iteratively transforming irregular scene text into a front-horizontal view , resulting in the significant performance improvement of scene text recognition . ( 2 ) In “ Ordinal distribution regression for gait-based age estimation ” , by considering the ordinal relationship of ages as an important cue , Zhu et al. design a neural network for gait-based age estimation by a new loss function termed as ordinal distribution loss . Significant predictors included general word reading , general set for variability performance , and item-specific set for variability performance ; word frequency and spelling to pronunciation transparency rating ; and an interaction between word reading and the transparency rating . Results underscore the importance of considering both general and item-specific factors affecting irregular word reading . As a case study , we analyzed classification of CT imaging reports into binary categories . In addition to regular text classification , we utilized topic modeling of the entire dataset in various ways . Topic modeling of the corpora provides interpretable themes that exist in these reports . To the best of our knowledge , in touch with the causality tasks , this is the first time that the Relation Network is applied . The experimental results on the AltLex dataset , demonstrate that : a ) MCDN is highly effective for the ambiguous and implicit causality inference ; b ) comparing with the regular text classification task , causality detection requires stronger inference capability ; c ) the proposed approach achieved state-of-the-art performance . With a degree of deformation of characters over 30 % , there is a high probability of false character recognition . The advantages of the method of text character recognition based on competing cellular automata include simplicity of rules of engagement , ability to parallelize the process of recognition easily , capability of recognition of distorted and partially overlapping characters that are the basis of modern captcha systems In order to implement the proposed algorithms , we created modeling software . It allowed us to evaluate effectiveness of the cellular-automaton algorithms to conduct experiments on text character recognition using the English alphabet and to demonstrate a number of advantages in comparison with other methods . We investigated a description of the solution to the problem on selecting the structural attributes in the images of text characters , which directly affect the quality of recognition . However , detecting vehicles with consistently high accuracy is difficult because there are various vehicle types with different appearances , such as sedans , buses , trucks , and SUVs . This intra-class variation must be addressed or , irregular recognition performance can occur , depending on vehicle type . Conventional machine learning-based algorithms are inadequate to address this problem because they are mostly trained on samples of entire appearance . However , recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment , which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information . In this paper , we develop a Sequence-to-Sequence Domain Adaptation Network ( SSDAN ) for robust text image recognition , which could exploit unsupervised sequence data by an attention-based sequence encoder-decoder network . In the SSDAN , a gated attention similarity ( GAS ) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment . Since BESOM is based on Bayesian network , it inherently allows bi-directional information flow , meaning that it can naturally merge information extracted from concrete data with highly-abstract prior knowledge . As an example of such kind of tasks , we report robust text recognition task with context information . We show that word spelling knowledge and word n-gram could be represented as a part of the network and they contribute the text recognition accuracy with noisy text images . After understanding scene texts , they can be used in many areas , such as text - based retrieval , translation , etc . There are usually two key steps to understand scene texts : text detection and text recognition . This paper focuses on scene text detection . An interpretable generative model for handwritten digits synthesis is proposed in this work . Modern image generative models , such as Generative Adversarial Networks ( GANs ) and Variational Autoencoders ( VAEs ) , are trained by backpropagation ( BP ) . The training process is complex and the underlying mechanism is difficult to explain . We propose an interpretable multi-stage PCA method to achieve the same goal and use handwritten digit images synthesis as an illustrative example . First , we derive principal-component-analysis-based ( PCA-based ) transform kernels at each stage based on the covariance of its inputs . An interpretable generative model for handwritten digits synthesis is proposed in this work . Modern image generative models such as the variational autoencoder ( VAE ) are trained by backpropagation ( BP ) . The idea is similar to generative adversarial networks except that the generators and discriminators are trained to work accordingly . Our experiments on hand-written digit generation and facial expression generation show that GCN 's two cooperative counterparts ( the generator and the classifier ) can work together nicely and achieve promising results . We also discovered a usage of such generative model as an data-augmentation tool . Deep belief network ( DBN ) has been shown to be a good generative model in tasks such as hand-written digit image generation . Previous work on DBN in the speech community mainly focuses on using the generatively pre-trained DBN to initialize a discriminative model for better acoustic modeling in speech recognition ( SR ) . In the field of deep neural networks , several generative methods have been proposed to address the challenges from generative and discriminative tasks , e.g. , natural language process , image caption and image generation . In this paper , a conditional recurrent variational autoencoder is proposed for multi-digit image synthesis . This model is capable of generating multi-digit images from the given number sequences and retaining the generalisation ability to recover different types of background .
In this context , we choose artificial neural network and make it much more tolerant to anomalies in the recorded image or data . Common optical character recognition tasks involve identifying simple edge detection and matching them with predefined patterns . In this research , characters are recognized even when noise such as inclination and skewedness presents , by training the network to look for discrepancies in data and relate them using vocabulary , grammar and common recurrences that may occur after a character .
Many researchers have been developed the methods for recognising characters in by using Optical Character Recognition methods . Although text recognition problem using Optical Character Recognition has been more or less solved , most of the Optical Character Recognition problem explored is belong to Latin alphabet texts . Meanwhile , there are several languages have non-Latin scripts as the written text .
In an optical character recognition problem , we compare ( as a function of training set size ) the performance of three neural network based ensemble methods ( two versions of boosting and a committee of neural networks trained independently ) to that of a single network . In boosting , the number of patterns actually used for training is a subset of all potential training patterns .
A script independent character segmentation from word images technique has been reported here . Word to character segmentation is an important preprocessing step of optical character recognition process . But in case of handwritten text , presence of touching characters decreases the accuracy of the technique of the segmentation of the characters from the word .
Unfortunately , contemporary methods of digitization and character recognition are not sufficient enough . During optical character recognition process , low quality of ancient manuscripts and on the other hand not enough advanced software result in numerous errors in output texts . The paper presents the results of the tests with ancient manuscripts in Polish , Latin , and English languages .
With the development of optical character recognition research , many techniques for printed character recognition have been developed . But the character recognition study for the New Tai Lue has lagged behind .
This paper presents a new comprehensive database for isolated offline handwritten Farsi/Arabic numbers and characters for use in optical character recognition research . The database is freely available for academic use .
Deep neural networks help solving different images related tasks very efficiently , though their cost is high whereas a lot of data are required for training . While there is a great demand to build neural network models for optical character detection and recognition for different languages , such as , for mobile real-time applications , datasets collecting and labeling are quite expensive . In this paper , we propose the fully automated approach for synthetic images with text generation based on deep learning and projective geometry methods .
In recent years , Optical Character Recognition(OCR ) is widely used in machine vision . In this paper , we investigated the problem of optical character detection and recognition for Image-based in natural scene . The Optical Character Recognition is divided into three steps : ( 1 ) Selecting the candidate regions through image preprocessing .
This provides the theoretical foundation for performance extrapolation based on pseudolikelihood , unbiased estimation , and high-dimensional asymptotics . We investigate the robustness of our methods to non-generative classifiers in simulations and one optical character recognition example .
The invention provides a device and a method for optical character recognition of images . The method includes : finding out an edge contour of a target character from a to-be-processed image ; during binarization processing of the target character , respectively taking each pixel point in the edge contour of the target character as a current pixel point to judge whether the current pixel point belongs to the target character or a background image of the target character ; if the current pixel point belongs to the target character , subjecting the current pixel point to binarization processing ; subjecting the image to character recognition after binarization processing .
It works for different types of degraded document images . Then an artificial neural network is used for optical character recognition purpose .
Degraded historical document images face many challenges in the process of optical character recognizing or word spotting , even after applying the traditional binarization techniques . In this paper , we propose a K-means based clustering technique for adaptive binarization of degraded document images .
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition . In this paper , Off-line Handwritten English Character recognition is done using Artificial Neural Network ( ANN ) .
This paper is aimed at clarifying the role of handwriting recognition in accordance with today 's maturing technologies . It tries to list and clarify the components that build handwriting recognition and related technologies such as OCR ( Optical Character Recognition ) and Signature Veriication . This paper could also be regarded as a survey of handwriting recognition and related topics with a rich list of references for the interested reader .
This paper presents a robust approach to extracting and summarizing the textual content of instructional videos for handwritten recognition , indexing and retrieval , and other e-learning applications . Content extraction from instructional videos is challenging due to image noise , light condition changes , camera movements , and unavoidable occlusions by instructors .
Thus , one can for example , describe the position of intervals where a curve or a path is decreasing to the right or increasing to the right compared to a reference . And therefore , one returns to the type of description made in handwritten recognition where one has described relatively the respective position of intervals end numbers in a predefined order .
In a handwriting recognition problem , characters can be represented using chain codes . The main problem in representing characters using chain code is optimizing the length of the chain code .
Stability and style-variation are important characteristics in handwriting analysis and recognition . This paper describes a stability modelling technique of handwritings in the context of on-line signature verification .
Researchers in the fields of document processing and handwriting analysis and recognition increasingly need to construct elaborate software systems to investigate and evaluate new research components such as cursive handwriting algorithms , contextual recognition systems and document parsing tools . Although a number of commercial and public-domain image processing frameworks already exist , for example Khoros , KBVision and the Image Understanding Environment ( IUE ) none of these systems fully supports the cycle of research , software development , informal and formal evaluation which is typically required by the research community .
Digitization though is only the first step to make the information contained in manuscripts accessible to researchers and to the interested public . What we create after digitization is only a “ digital image ” of the page and further processing steps need to be applied during the handwriting recognition process , so that the manuscript ’s content is transformed into a form that is interpretable by a computer . One important step in the handwriting recognition process is that of text line extraction , which aims at extracting individual text lines from the manuscript page .
It is difficult to identify the Arabic numerals'handwriting , especially the feature extraction and the handwriting characteristics recognition . The classification of features of traditional Arabic numerals handwriting are set up according to Chinese handwriting recognition feature classification , rather than the characteristics of the Arabic numerals .
Consequently , any sorting algorithm , coupled with such a neural comparator , can sort any set of items . Although applications include ink segmentation ( for handwriting recognition purposes ) and Web page ranking , our emphasis on this paper is on classifier aggregation and , in particular , the integration of our standard handwriting recognizers with a user personalization database that consists of user samples
Handwritten digits recognition is a well-researched sub area within the field of handwritten writing recognition , which is concerned with learning models to distinguish presegmented handwritten digits . However , handwritten digit recognition is still a difficult task because of the high variability in the digit shapes written by individuals .
The Another majorareaofapplihation isthefaerecognition ( 24 , larger training datasetsareabigboontothese systems asthe 261an other biometrics . TheHandwriting recognition ( 7,11 performhance getsbetter andbetter withtheincrease indatasets . also Thehigher training datasetalthough drastically increases the .
[ reference ] has shown Convolutional Neural Networks can perform well like MRFs / CRFs approaches in image restoration application . Another domain which benefits from the combination of CNNs and structured loss is handwriting recognition . In natural language processing , Yao et al .
paragraph : Connection to multidimensional RNNs . Multidimensional RNNs ( MDRNN ) are an extension of 1D sequential RNNs that can accept multidimensional input e.g. images , and have been successfully applied to image segmentation and handwriting recognition . Notably multidimensional LSTMs ( MDLSTM ) have an analogous formulation to ours except the term and the fact that we use distinct weights per column ( or ‘ layer ’ in our case ) .
Another advantage of ANNs often used to justify their use is the ease of hardware implementation ; however , in most publications , this did not seem to be the reason for application . The experiment on supervised classification , in handwritten digit recognition , showed that ANNs are quite capable of solving difficult object recognition problems . A number of ANN architectures were trained to mimic the Kuwahara filter , a nonlinear edge-preserving smoothing filter used in preprocessing .
We also present a distance function that is used to measure the distance between an instance and a prototype . The new methods are compared with some of the best-known reduction techniques on the handwritten digit recognition problem . The results show that the techniques presented provide the best combination of amount of reduction and classification accuracy for both Arabic and Hindu digits .
Convolutional Neural Network ( CNN ) , a popular machine learning algorithm , has been proven as a highly accurate and effective algorithm that has been used in a variety of applications such as handwriting digit recognition , visual recognition , and image classification . State-of-the-art CNNs are computationally intensive , yet their parallel and modular nature make platforms like Field Programmable Gate Arrays ( FPGAs ) well suited for the acceleration process .
Compared with the previous works where Mahalanobis distance metric and instance weights are learned by using the labelled data or in a pipelined framework that leads to a decrease in the performance , our proposed SSMT attempts to learn a regularized distance metric and instance weights by considering unlabelled data in a parallel framework . Experimental evaluation on three cross-domain visual data sets , e.g. , PIE Face , Handwriting Digit Recognition on MNIST – USPS and Object Recognition , demonstrates the effectiveness of our designed approach on facilitating the unlabelled target task learning , compared to current state-of-the-art domain adaptation approaches .
Moreover , it allows to incorporate a priori and application-driven knowledge into the basis vectors , during the learning process . We apply our dimensionality reduction method to images and compare it with principal component analysis ( PCA ) and non-negative matrix factorization ( NMF ) and its variants , in the context of handwritten digit image recognition and face recognition . The experimental results suggest that the proposed dimensionality reduction algorithm is competitive to PCA and NMF and that it results into meaningful features with high discriminant value .
A solution using neural network to achieve preconditioned selection is suggested . Two examples on handprinted digit recognition are used to illustrate the potential gain .
A modular and structured approach was taken in order to simplify network construction , optimization and analysis . Results on the task of handprinted digit recognition are among the best report to date on a set of real-world ZIP code digit images , provided by the United States Postal Service . The system achieved a 99.1 % recognition rate on the training set and a 96.0 % recognition rate on the test set with no rejections .
After cutting the stem then performed feature extraction to obtain a vector which was fed to the neural network system for recognition rate . System back propagation neural network for handwritten digit pattern recognition was designed by 168neuron consists of input layer , 70 neurons in the hidden layer and 10 neurons in the output layer . The results obtained in this study were 83.78 % of the data slip can be recognized correctly .
A function quantifying suitability of a selected subset is proposed , and a greedy algorithm for solving the subset selection problem is introduced . The algorithm is evaluated on hand digit recognition and other binary classification tasks , and its performance is compared to stratified sampling methods .
We investigate the origin of the significantly different error rates between handwritten digit machine recognition and vowel sound machine recognition . While the error rate for five Korean vowel sounds , [ ɑ ] , [ ʊ ] , [ ɪ ] , [ ο ] , and [ ɛ ] , is about 10 percent , that of handwritten digit recognition is less than 1 percent for convolutional neural networks ( CNNs ) with raw data .
Convolutional neural networks have gained much recent attention due to their success in image classification . Convnets trained with backpropagation were initially succesful in digit recognition and OCR . The feature representations learned from large data sets have been found to generalize well to other image classification tasks and even to object detection .
subsection : Related work A first attempt to remove from GRUs has recently led to a single - gate architecture called Minimal Gated Recurrent Unit ( M - GRU ) , that achieves a performance comparable to that obtained by standard GRUs in handwritten digit recognition as well as in a sentiment classification task . To the best of our knowledge , our contribution is the first attempt that explores this architectural variation in speech recognition .
A word spotting understanding server was implemented within the GALAXY [ 4 ] architecture using the JUPITER [ 3 ] weather information domain . Word spotting was implemented using a simple context-free grammar that maps words to key-value classes . Word spotting was evaluated by comparing understanding error rates against those of the TINA [ 17 ] natural language understanding component .
word spotting is a technique which can extract the text from input image . This paper addresses the problems of word spotting and word recognition on images . . In recognition , the goal is to recognize the content of the word image , usually aided by a dictionary or lexicon .
We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying . More precisely , a model is trained “ on the fly ” with images rendered from the searched words in one or multiple computer fonts .
In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task . The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into ( i ) a codebook of shapes and ( ii ) a set of word-specific parameters .
However , in spite of the great progress achieved , handwritten document word spotting still can hardly achieve acceptable performance on real-world handwritten document images that vary widely in writing style and quality . This paper gives an overview of published research efforts in the area of handwritten document image word spotting and on the technologies used in the field . We first start by describing a general model for document word spotting followed by discussing present challenges in handwritten document word spotting . Then the used databases for handwritten document word spotting and other handwritten text tasks are discussed . After that , research works on handwritten document word spotting are presented .
We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains . We then train the model concurrently on textual sentiment analysis , speech recognition , image classification , action recognition from video , and handwriting word spotting of two different scripts ( Arabic and English ) . The model we propose successfully learned different tasks across multiple domains .
Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes . The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples ; word-retrieval is therefore very difficult in low-resource scripts .
Recommendations are also provided for future research . It is also observed that the research in this area is quietly thin and still more research is to be done , particularly in the case of word recognition of printed/handwritten documents in Indic scripts .
In this paper , we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text . Image moments provide robust estimates for text characteristics such as size and position of words within an image .
The secondary ( or artistic ) features are the parts of a character which contribute to its various representations . The reduced character methodology presented in this paper attempts to prove that the recognition of a reduced size character provides a robust approach for recognition of handwritten text . The RNN approach for handwritten character recognition is based upon recurrent neural networks .
Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results . Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition .
Many existing approaches incorporate sophisticated network structures to handle various shapes , use extra annotations for stronger supervision , or employ hard-to-train recurrent neural networks for sequence modeling . In this work , we propose a simple yet robust approach for irregular text recognition . With no need to convert input images to sequence representations , we directly connect two-dimensional CNN features to an attention-based sequence decoder .
All six research papers achieve the significant progresses in their corresponding vision tasks . ( 1 ) In “ Progressive rectification network for irregular text recognition ” , Gao et al. propose a progressive rectification network ( PRN ) for iteratively transforming irregular scene text into a front-horizontal view , resulting in the significant performance improvement of scene text recognition . ( 2 ) In “ Ordinal distribution regression for gait-based age estimation ” , by considering the ordinal relationship of ages as an important cue , Zhu et al. design a neural network for gait-based age estimation by a new loss function termed as ordinal distribution loss .
Significant predictors included general word reading , general set for variability performance , and item-specific set for variability performance ; word frequency and spelling to pronunciation transparency rating ; and an interaction between word reading and the transparency rating . Results underscore the importance of considering both general and item-specific factors affecting irregular word reading .
As a case study , we analyzed classification of CT imaging reports into binary categories . In addition to regular text classification , we utilized topic modeling of the entire dataset in various ways . Topic modeling of the corpora provides interpretable themes that exist in these reports .
To the best of our knowledge , in touch with the causality tasks , this is the first time that the Relation Network is applied . The experimental results on the AltLex dataset , demonstrate that : a ) MCDN is highly effective for the ambiguous and implicit causality inference ; b ) comparing with the regular text classification task , causality detection requires stronger inference capability ; c ) the proposed approach achieved state-of-the-art performance .
With a degree of deformation of characters over 30 % , there is a high probability of false character recognition . The advantages of the method of text character recognition based on competing cellular automata include simplicity of rules of engagement , ability to parallelize the process of recognition easily , capability of recognition of distorted and partially overlapping characters that are the basis of modern captcha systems
In order to implement the proposed algorithms , we created modeling software . It allowed us to evaluate effectiveness of the cellular-automaton algorithms to conduct experiments on text character recognition using the English alphabet and to demonstrate a number of advantages in comparison with other methods . We investigated a description of the solution to the problem on selecting the structural attributes in the images of text characters , which directly affect the quality of recognition .
However , detecting vehicles with consistently high accuracy is difficult because there are various vehicle types with different appearances , such as sedans , buses , trucks , and SUVs . This intra-class variation must be addressed or , irregular recognition performance can occur , depending on vehicle type . Conventional machine learning-based algorithms are inadequate to address this problem because they are mostly trained on samples of entire appearance .
However , recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment , which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information . In this paper , we develop a Sequence-to-Sequence Domain Adaptation Network ( SSDAN ) for robust text image recognition , which could exploit unsupervised sequence data by an attention-based sequence encoder-decoder network . In the SSDAN , a gated attention similarity ( GAS ) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment .
Since BESOM is based on Bayesian network , it inherently allows bi-directional information flow , meaning that it can naturally merge information extracted from concrete data with highly-abstract prior knowledge . As an example of such kind of tasks , we report robust text recognition task with context information . We show that word spelling knowledge and word n-gram could be represented as a part of the network and they contribute the text recognition accuracy with noisy text images .
After understanding scene texts , they can be used in many areas , such as text - based retrieval , translation , etc . There are usually two key steps to understand scene texts : text detection and text recognition . This paper focuses on scene text detection .
An interpretable generative model for handwritten digits synthesis is proposed in this work . Modern image generative models , such as Generative Adversarial Networks ( GANs ) and Variational Autoencoders ( VAEs ) , are trained by backpropagation ( BP ) . The training process is complex and the underlying mechanism is difficult to explain . We propose an interpretable multi-stage PCA method to achieve the same goal and use handwritten digit images synthesis as an illustrative example . First , we derive principal-component-analysis-based ( PCA-based ) transform kernels at each stage based on the covariance of its inputs .
An interpretable generative model for handwritten digits synthesis is proposed in this work . Modern image generative models such as the variational autoencoder ( VAE ) are trained by backpropagation ( BP ) .
The idea is similar to generative adversarial networks except that the generators and discriminators are trained to work accordingly . Our experiments on hand-written digit generation and facial expression generation show that GCN 's two cooperative counterparts ( the generator and the classifier ) can work together nicely and achieve promising results . We also discovered a usage of such generative model as an data-augmentation tool .
Deep belief network ( DBN ) has been shown to be a good generative model in tasks such as hand-written digit image generation . Previous work on DBN in the speech community mainly focuses on using the generatively pre-trained DBN to initialize a discriminative model for better acoustic modeling in speech recognition ( SR ) .
In the field of deep neural networks , several generative methods have been proposed to address the challenges from generative and discriminative tasks , e.g. , natural language process , image caption and image generation . In this paper , a conditional recurrent variational autoencoder is proposed for multi-digit image synthesis . This model is capable of generating multi-digit images from the given number sequences and retaining the generalisation ability to recover different types of background .
Optical character recognition using artificial neural network Implementation of Optical Character Recognition using Tesseract with the Javanese Script Target in Android Application Boosting and Other Machine Learning Algorithms A Script Independent Technique for Extraction of Characters from Handwritten Word Images Most Frequent Errors in Digitization of Polish Ancient Manuscripts Printed New Tai Lue character recognition based on BP neural network A Comprehensive Isolated Farsi/Arabic Character Database for Handwritten OCR Research Synthetic images generation for text detection and recognition in the wild Optical Character Detection and Recognition for Image-Based in Natural Scene How many faces can be recognized? Performance extrapolation for multi-class classification Device and method for optical character recognition of images Binarization and Character Recognition of Degraded Printed Documents Handwritten document image binarization: An adaptive K-means based approach A Novel Neural Net based Off-line English Character Recognition System An Overview of Handwriting Recognition Content Extraction and Summarization of Instructional Videos Computing with words =? New mathematical theory of descriptions. Part 1: description of paths (spiral data, Kolmogorov theorem and neural networks, chess, salesman) Recognition of Isolated Handwritten Latin Characters using One Continuous Route of Freeman Chain Code Representation and Feedforward Neural Network Classifier On-line signature verification based on stroke matching A handwriting understanding environment (HUE) for research in document and handwriting processing Binarization-free Text Line Extraction for Historical Manuscripts The Discussion on the Handwriting Characteristics of Arabic Numbers Comparative Classifier Aggregation Handwritten digit recognition using elastic matching FuzzyNeuroSystems forMachine Learning for Large DataSets Conditional Random Fields as Recurrent Neural Networks Cell-aware Stacked LSTMs for Modeling Sentences Nonlinear image processing using artificial neural networks Instance Reduction Techniques for ِand Hindu Digit Recognition VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT) A framework for semi-supervised metric transfer learning on manifolds Dimensionality Reduction with Adaptive Approximation Dynamic classifier combination using neural network Character Recognition Using A Modular Spatiotemporal Connectionist Model Ekstraksi Ciri Batang untuk Pengenalan Nomer Rekening Tulisan Tangan dengan Jaringan Syaraf Tiruan Perambatan Balik Data subset selection for efficient SVM training Origin of the Higher Difficulty in the Recognition of Vowels Compared to Handwritten Digits in Deep Neural Networks Do Convnets Learn Correspondence? Light Gated Recurrent Units for Speech Recognition Building a speech understanding system using word spotting techniques An Efficient Word Recognition System Based on Effective Classification Method Handwritten Word Image Retrieval with Synthesized Typed Queries Unsupervised writer adaptation of whole-word HMMs with application to word-spotting A Survey on handwritten documents word spotting Compact and Efficient Multitask Learning in Vision, Language and Speech Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning A comprehensive survey on word recognition for non-Indic and Indic scripts Moment-Based Image Normalization for Handwritten Text Recognition Handwriting recognition using a reduced character method and neural nets An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors A Simple and Robust Convolutional-Attention Network for Irregular Text Recognition Special focus on deep learning for computer vision The Role of Set for Variability in Irregular Word Reading: Word and Child Predictors in Typically Developing Readers and Students At-Risk for Reading Disabilities Topic Modeling Based Classification of Clinical Reports A Multi-level Neural Network for Implicit Causality Detection in Web Texts Development of a system for graphic captcha systems recognition using competing cellular automata Realization of information technology of character recognition based on competing cellular automata Vehicle recognition using common appearance captured by 3D LIDAR and monocular camera Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition Context-Dependent Robust Text Recognition using Large-scale Restricted Bayesian Network R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection An Interpretable Generative Model for Handwritten Digit Image Synthesis. An Interpretable Generative Model for Handwritten Digits Synthesis Generative Cooperative Net for Image Generation and Data Augmentation Multi-distribution deep belief network for speech synthesis Multi-digit image synthesis using recurrent conditional variational autoencoder
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ICML ACIIDS (1) ICIC (3) ICIP DH ICPR (3) ICWET 2015 IEEE International Conference on Computer Vision (ICCV) ACML ICME Document Recognition EUSIPCO NIPS IEEE Transactions on Emerging Topics in Computational Intelligence ICDAR ICCV Workshops ICFHR ACL (Student Research Workshop) Intelligent Vehicles Symposium CVPR ArXiv ICIP ICASSP IJCNN
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