ATE shows encouraging results which can be efficiently used in image spam filtering . Besides its robustness against obfuscating methods in image spam , ATE shows efficient performance when used for scene text detection . Scene text detection and recognition have various applications in real world especially in navigation , data retrieval , helping visual impaired groups , package labelling in industrial automation , logistics and others . Due to its wide application areas , this research focuses on detecting and recognizing horizontal and multi-oriented text with a hope of further improving the accuracy . In this paper , we introduce the Equipment Nameplate Dataset , a large dataset for scene text detection and recognition . Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded , which makes our dataset more challenging than other datasets . Camera-based text processing has attracted considerable attention and numerous methods have been proposed . However , most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images . In this paper , we present a text-line detection algorithm for camera-captured document images , which is an essential step toward document understanding . There are many video images where hand written text may appear . Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing , retrieval etc . Also there are many video frames where text line may be multi-oriented in nature . We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm , including text classification , text detection , and character identification . The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene text classification and detection , and significantly outperforms the existing algorithms for character identification . Moreover , the same text instances with various viewpoints can be produced by randomly moving and rotating the virtual camera , which acts as human eyes . The experiments on the standard scene text detection benchmarks using the generated synthetic data demonstrate the effectiveness and superiority of the proposed method . We show how to achieve object detection through constructing Markov Random Fields , mapping parameters and performing approximations using advanced inference and learning algorithms . Third , we explore a higher-order relational model and efficient inference algorithms for the region labeling problem , using video scene text detection as a test case . Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments . The concatenated features are then used for further detection . - Our modification of Faster R - CNN also include adding a smaller anchor for detecting small scene texts and using inclined non - maximum suppression to post - process the detection candidates to get the final result . section : Related Work Deep learning based scene text detection methods [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] have been studied and achieve better performance than traditional methods . TextBoxes is an end - to - end fast scene text detector with a single deep neural network [ reference ] . DeepText generates word region proposals via Inception - RPN and then scores and refines each word proposal using the text detection network [ reference ] . Fully - Convolutional Regression Network ( FCRN ) utilizes synthetic images to train the model for scene text detection [ section : Conclusion In this paper , we introduce a Rotational Region CNN ( R 2 CNN ) for detecting scene texts in arbitrary orientations . The framework is based on Faster R - CNN architecture [ reference ] . The RPN is used to generate axis - aligned region proposals . section : Introduction Recently , natural scene text detection has attracted extensive attention for its numerous applications , such as scene understanding , product identification , automatic driving and target geolocation . However , due to the large variations in foreground texts and background objects , and the diverse text variabilities in shapes , colors , fonts , orientations and scales , along with the extreme illumination and occlusion , text detection in natural scene is still faced with considerable challenges . However , they usually fall short when dealing with challenging scenarios , even when equipped with deep neural network models , because the overall performance is determined by the interplay of multiple stages and components in the pipelines . In this work , we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes . The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images , eliminating unnecessary intermediate steps ( e.g. , candidate aggregation and word partitioning ) , with a single neural network . To solve this problem , we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective . We design a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection . Extensive experiments on ICDAR2015 , RCTW-17 , and MSRA-TD500 datasets demonstrate our method 's superiority in terms of both effectiveness and efficiency . Multi-oriented text detection is not easy as detection of captions or graphics which is usually appears in horizontal direction and has high contrast compared to its background . Multi-oriented text detection generally refers to scene text that makes text detection more challenging . Therefore conventional text detection may not give good results for multi-oriented video frames text detection . Hence , in this paper , we present a new enhancement method that includes the product of Laplacian and Sobel operations to enhance text pixels in videos . However , the non-text pixel information in these regions easily results in the poor performance of text detection , because these semantic segmentation methods need accurate pixel-level annotated training data to achieve approving performance and they are sensitive to noise and interference . In this work , we propose a fully convolutional network ( FCN ) based method termed TextEdge for multi-oriented scene text detection . Compared with previous methods simply using bounding-box regions as a segmentation mask , TextEdge introduces the text-region edge map as a new segmentation mask . Abstract Due to the complex backgrounds , size variations , and changes in perspective and orientation in natural scene images , detecting multi-oriented text is a difficult problem that has recently attracted considerable attention from research communities . In this paper , we present a novel method that effectively and robustly detects multi-oriented text in natural scene images . Especially in recent years , the researchers has seen a flow of research efforts and considerable developments in these fields , however many challenges e.g. low resolution , complex background and variations in colors , font , font size , Multi-orientations , Multi-orientation text movements , noise , blur , and distortion still remain . The objectives of this work are in four folds : ( 1 ) to introduce a new descriptor called Histogram Oriented Moments ( HOM ) for detecting multi-oriented text from videos . The HOM is created by considering the orientations calculated with the second order geometrical moments . Multi-oriented and multi-lingual scene text detection plays an important role in computer vision area and is challenging due to the wide variety of text and background . In this paper , first , we point out the two key tasks when extending convolutional neural network (CNN)-based object detection frameworks to scene text detection . It detects and segments the text instance jointly and simultaneously , leveraging merits from both semantic segmentation task and region proposal based object detection task . Not involving any extra pipelines , our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks : ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1 % and 82.0 % respectively . The central text pixels always constrained the text box in the process of direct regression , which made the predicted results inaccurate . To solve this problem , we propose a novel Location Sensitive Regression algorithm for multi-oriented scene Text Detection ( LSRTD ) , which assigns different weights to text pixels according to their distance to text box vertexes . At the same time , we apply Focal Loss to pixel-wise text/non-text classification task , the number of false positive cases in results is decreased because of Focal Loss balances the loss weights of easy and hard samples during training . Given a proposal generated by a region proposal network ( RPN ) , instead of directly predicting the bounding box coordinates of the concerned text instance , the proposal is enlarged to create a search region so that an “ In-Out ” conditional probability to each row and column of this search region is assigned , which can then be used to accurately infer the concerned bounding box . Furthermore , we present a simple yet effective two-stage approach to convert the difficult multi-oriented text detection problem to a relatively easier horizontal text detection problem , which makes our approach able to robustly detect multi-oriented text instances with accurate bounding box localization . Experiments demonstrate that the proposed approach boosts the localization accuracy of Faster R-CNN based text detectors significantly . In this paper , we propose a method based on the skeletonization operation for multi-oriented video text detection . The first step uses our existing Laplacian-based method to identify candidate text regions . However , current approaches could not keep a good balance between accuracy and speed , i.e. , a high-performance accuracy but with a low processing speed , or vice-versa . In this paper , we propose a novel model , named PyrBoxes , for efficient and effective multi-scale scene text detection . PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes , and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection . document : Fused Text Segmentation Networks for Multi - oriented Scene Text Detection In this paper , we introduce a novel end - end framework for multi - oriented scene text detection from an instance - aware semantic segmentation perspective . Therefore , it reports relatively lower accuracies in ICDAR 2015 Competition Challenge 4 âIncidental scene text localizationâ compared to horizontal scene text detection benchmarks . Recently , a few approaches have been proposed to address the multi - oriented text detection . In general , there are currently four different types of methods . Mask - NMS is introduced to improve the standard NMS when facing heavily inclined or line - level text instances . Without many bells and whistles , our approach outperforms state of the art on current multi - oriented text detection benchmarks . section : Related work Ground truth of each text instance is presented by bounding boxes and masks shown in Fig.3 . In most multi - oriented text detection dataset , annotations are given in quadrilaterals such as IC15 or can be converted to quadrilaterals such as TD500 . For each instance , we directly generate mask from quadrilateral coordinates and use the minimal rectangle containing the mask as the bounding box . [ reference ] . It is good at detecting horizontal texts but not fit for high inclined texts . The method based on Fully Convolutional Network ( FCN ) is designed to detect multi - oriented scene texts [ reference ] [ reference ] is proposed for multi - oriented scene text detection . Our goal is to detect arbitrary - oriented scene texts . The proposed approach deals with the text region appearing not only in horizontal or vertical directions , but also in any other oblique or curved orientation in the image . Until now only a few methods have been proposed that look into curved text detection in video frames , wherein lies our novelty . In our approach , we first apply Fourier – Laplacian transform on the image followed by an ideal Laplacian – Gaussian filtering . With the help of recent deep learning techniques , multi-oriented text detection that was considered to be quite challenging has been solved to some extent . However , most existing methods still perform poorly for curved text detection , mainly due to the limitation of their text representations ( e.g. , horizontal boxes , rotated rectangles or quadrangles ) . To solve this problem , we propose a novel method to detect irregular scene texts based on instance-aware segmentation . Therefore cameracaptured documents need to go through dewarping before being processed by traditional text recognition methods . Curled textline detection is an important step of dewarping . Previous approaches of curled textline detection use binarization as a pre-processing step , which can negatively affect the detection results under uneven shading . Proposed approach explores a new framework for curved video text detection and extraction . The algorithm first utilizes a Gaussian filter based Color Edge Enhancement followed by a Gray level Co-occurrence matrix feature extraction method for text detection . Previous approaches of binarization and curled textline detection are sensitive to distortions and loose some crucial image information during each step , which badly affects dewarping and further processing . Here we introduce a novel algorithm for curled textline region detection directly from a grayscale camera-captured document image , in which matched filter bank approach is used for enhancing textline structure and then ridges detection is applied for finding central line of curled textlines . The resulting ridges can be potentially used for binarization , dewarping or designing new techniques for camera-captured document image processing . Video text analysis is challenging due to varying background , multiple orientations and low contrast between text and non-text regions . Proposed approach explores a new framework for curved video text detection and recognition where from the observation that curve text regions can be well defined by edges size and uniform texture , Probable curved text edge detection is accomplished by processing wavelet sub bands followed by text localization by utilizing fast texture descriptor LU-transform . Binarization is achieved by maximal H-transform . Due to complex background and unpredictable characteristics of scene text and noise , text characters in strings are often touching that affects the performance of segmentation and recognition . This paper presents a novel approach for curved text recognition using Hidden Markov Models ( HMM ) . From curved text , a path of sliding window is estimated and features extracted from the sliding window are fed to the HMM system for recognition . This paper presents the method based on image processing algorithms for segmentation and extraction of characters from curled text lines from document images . The algorithm performs the curved text segmentation using x-line and base line . The words in the document image are identified and bounding boxes are plotted around the words . However , besides the quadrilateral shape , there are many other various shapes of text line in natural scene . Therefore , recent research have begun to explore curve text line detection . In this paper we explore both arbitrary - oriented and curve text detection . Our method named sliding line point regression ( SLPR ) is based on 2 - step object detection methods using Faster R - CNN or R - FCN . To show the effectiveness of our proposed PSENet , we conduct extensive experiments on three competitive benchmark datasets including ICDAR 2015 , ICDAR 2017 MLT and SCUT - CTW1500 . Among these datasets , SCUT - CTW1500 is explicitly designed for curve text detection , and on this dataset we surpass the previous state - of - the - art result by absolute 6.37 % . Furthermore , the proposed PSENet achieves better or at least comparable performance on the ordinary quadrangular text datasets : ICDAR 2015 and ICDAR 2017
ATE shows encouraging results which can be efficiently used in image spam filtering . Besides its robustness against obfuscating methods in image spam , ATE shows efficient performance when used for scene text detection .
Scene text detection and recognition have various applications in real world especially in navigation , data retrieval , helping visual impaired groups , package labelling in industrial automation , logistics and others . Due to its wide application areas , this research focuses on detecting and recognizing horizontal and multi-oriented text with a hope of further improving the accuracy .
In this paper , we introduce the Equipment Nameplate Dataset , a large dataset for scene text detection and recognition . Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded , which makes our dataset more challenging than other datasets .
Camera-based text processing has attracted considerable attention and numerous methods have been proposed . However , most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images . In this paper , we present a text-line detection algorithm for camera-captured document images , which is an essential step toward document understanding .
There are many video images where hand written text may appear . Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing , retrieval etc . Also there are many video frames where text line may be multi-oriented in nature .
We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm , including text classification , text detection , and character identification . The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene text classification and detection , and significantly outperforms the existing algorithms for character identification .
Moreover , the same text instances with various viewpoints can be produced by randomly moving and rotating the virtual camera , which acts as human eyes . The experiments on the standard scene text detection benchmarks using the generated synthetic data demonstrate the effectiveness and superiority of the proposed method .
We show how to achieve object detection through constructing Markov Random Fields , mapping parameters and performing approximations using advanced inference and learning algorithms . Third , we explore a higher-order relational model and efficient inference algorithms for the region labeling problem , using video scene text detection as a test case .
Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments .
The concatenated features are then used for further detection . - Our modification of Faster R - CNN also include adding a smaller anchor for detecting small scene texts and using inclined non - maximum suppression to post - process the detection candidates to get the final result . section : Related Work
Deep learning based scene text detection methods [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] have been studied and achieve better performance than traditional methods . TextBoxes is an end - to - end fast scene text detector with a single deep neural network [ reference ] . DeepText generates word region proposals via Inception - RPN and then scores and refines each word proposal using the text detection network [ reference ] . Fully - Convolutional Regression Network ( FCRN ) utilizes synthetic images to train the model for scene text detection [
section : Conclusion In this paper , we introduce a Rotational Region CNN ( R 2 CNN ) for detecting scene texts in arbitrary orientations . The framework is based on Faster R - CNN architecture [ reference ] . The RPN is used to generate axis - aligned region proposals .
section : Introduction Recently , natural scene text detection has attracted extensive attention for its numerous applications , such as scene understanding , product identification , automatic driving and target geolocation . However , due to the large variations in foreground texts and background objects , and the diverse text variabilities in shapes , colors , fonts , orientations and scales , along with the extreme illumination and occlusion , text detection in natural scene is still faced with considerable challenges .
However , they usually fall short when dealing with challenging scenarios , even when equipped with deep neural network models , because the overall performance is determined by the interplay of multiple stages and components in the pipelines . In this work , we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes . The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images , eliminating unnecessary intermediate steps ( e.g. , candidate aggregation and word partitioning ) , with a single neural network .
To solve this problem , we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective . We design a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection . Extensive experiments on ICDAR2015 , RCTW-17 , and MSRA-TD500 datasets demonstrate our method 's superiority in terms of both effectiveness and efficiency .
Multi-oriented text detection is not easy as detection of captions or graphics which is usually appears in horizontal direction and has high contrast compared to its background . Multi-oriented text detection generally refers to scene text that makes text detection more challenging . Therefore conventional text detection may not give good results for multi-oriented video frames text detection . Hence , in this paper , we present a new enhancement method that includes the product of Laplacian and Sobel operations to enhance text pixels in videos .
However , the non-text pixel information in these regions easily results in the poor performance of text detection , because these semantic segmentation methods need accurate pixel-level annotated training data to achieve approving performance and they are sensitive to noise and interference . In this work , we propose a fully convolutional network ( FCN ) based method termed TextEdge for multi-oriented scene text detection . Compared with previous methods simply using bounding-box regions as a segmentation mask , TextEdge introduces the text-region edge map as a new segmentation mask .
Abstract Due to the complex backgrounds , size variations , and changes in perspective and orientation in natural scene images , detecting multi-oriented text is a difficult problem that has recently attracted considerable attention from research communities . In this paper , we present a novel method that effectively and robustly detects multi-oriented text in natural scene images .
Especially in recent years , the researchers has seen a flow of research efforts and considerable developments in these fields , however many challenges e.g. low resolution , complex background and variations in colors , font , font size , Multi-orientations , Multi-orientation text movements , noise , blur , and distortion still remain . The objectives of this work are in four folds : ( 1 ) to introduce a new descriptor called Histogram Oriented Moments ( HOM ) for detecting multi-oriented text from videos . The HOM is created by considering the orientations calculated with the second order geometrical moments .
Multi-oriented and multi-lingual scene text detection plays an important role in computer vision area and is challenging due to the wide variety of text and background . In this paper , first , we point out the two key tasks when extending convolutional neural network (CNN)-based object detection frameworks to scene text detection .
It detects and segments the text instance jointly and simultaneously , leveraging merits from both semantic segmentation task and region proposal based object detection task . Not involving any extra pipelines , our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks : ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1 % and 82.0 % respectively .
The central text pixels always constrained the text box in the process of direct regression , which made the predicted results inaccurate . To solve this problem , we propose a novel Location Sensitive Regression algorithm for multi-oriented scene Text Detection ( LSRTD ) , which assigns different weights to text pixels according to their distance to text box vertexes . At the same time , we apply Focal Loss to pixel-wise text/non-text classification task , the number of false positive cases in results is decreased because of Focal Loss balances the loss weights of easy and hard samples during training .
Given a proposal generated by a region proposal network ( RPN ) , instead of directly predicting the bounding box coordinates of the concerned text instance , the proposal is enlarged to create a search region so that an “ In-Out ” conditional probability to each row and column of this search region is assigned , which can then be used to accurately infer the concerned bounding box . Furthermore , we present a simple yet effective two-stage approach to convert the difficult multi-oriented text detection problem to a relatively easier horizontal text detection problem , which makes our approach able to robustly detect multi-oriented text instances with accurate bounding box localization . Experiments demonstrate that the proposed approach boosts the localization accuracy of Faster R-CNN based text detectors significantly .
In this paper , we propose a method based on the skeletonization operation for multi-oriented video text detection . The first step uses our existing Laplacian-based method to identify candidate text regions .
However , current approaches could not keep a good balance between accuracy and speed , i.e. , a high-performance accuracy but with a low processing speed , or vice-versa . In this paper , we propose a novel model , named PyrBoxes , for efficient and effective multi-scale scene text detection . PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes , and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection .
document : Fused Text Segmentation Networks for Multi - oriented Scene Text Detection In this paper , we introduce a novel end - end framework for multi - oriented scene text detection from an instance - aware semantic segmentation perspective .
Therefore , it reports relatively lower accuracies in ICDAR 2015 Competition Challenge 4 âIncidental scene text localizationâ compared to horizontal scene text detection benchmarks . Recently , a few approaches have been proposed to address the multi - oriented text detection . In general , there are currently four different types of methods .
Mask - NMS is introduced to improve the standard NMS when facing heavily inclined or line - level text instances . Without many bells and whistles , our approach outperforms state of the art on current multi - oriented text detection benchmarks . section : Related work
Ground truth of each text instance is presented by bounding boxes and masks shown in Fig.3 . In most multi - oriented text detection dataset , annotations are given in quadrilaterals such as IC15 or can be converted to quadrilaterals such as TD500 . For each instance , we directly generate mask from quadrilateral coordinates and use the minimal rectangle containing the mask as the bounding box .
[ reference ] . It is good at detecting horizontal texts but not fit for high inclined texts . The method based on Fully Convolutional Network ( FCN ) is designed to detect multi - oriented scene texts [ reference ] [ reference ] is proposed for multi - oriented scene text detection . Our goal is to detect arbitrary - oriented scene texts .
The proposed approach deals with the text region appearing not only in horizontal or vertical directions , but also in any other oblique or curved orientation in the image . Until now only a few methods have been proposed that look into curved text detection in video frames , wherein lies our novelty . In our approach , we first apply Fourier – Laplacian transform on the image followed by an ideal Laplacian – Gaussian filtering .
With the help of recent deep learning techniques , multi-oriented text detection that was considered to be quite challenging has been solved to some extent . However , most existing methods still perform poorly for curved text detection , mainly due to the limitation of their text representations ( e.g. , horizontal boxes , rotated rectangles or quadrangles ) . To solve this problem , we propose a novel method to detect irregular scene texts based on instance-aware segmentation .
Therefore cameracaptured documents need to go through dewarping before being processed by traditional text recognition methods . Curled textline detection is an important step of dewarping . Previous approaches of curled textline detection use binarization as a pre-processing step , which can negatively affect the detection results under uneven shading .
Proposed approach explores a new framework for curved video text detection and extraction . The algorithm first utilizes a Gaussian filter based Color Edge Enhancement followed by a Gray level Co-occurrence matrix feature extraction method for text detection .
Previous approaches of binarization and curled textline detection are sensitive to distortions and loose some crucial image information during each step , which badly affects dewarping and further processing . Here we introduce a novel algorithm for curled textline region detection directly from a grayscale camera-captured document image , in which matched filter bank approach is used for enhancing textline structure and then ridges detection is applied for finding central line of curled textlines . The resulting ridges can be potentially used for binarization , dewarping or designing new techniques for camera-captured document image processing .
Video text analysis is challenging due to varying background , multiple orientations and low contrast between text and non-text regions . Proposed approach explores a new framework for curved video text detection and recognition where from the observation that curve text regions can be well defined by edges size and uniform texture , Probable curved text edge detection is accomplished by processing wavelet sub bands followed by text localization by utilizing fast texture descriptor LU-transform . Binarization is achieved by maximal H-transform .
Due to complex background and unpredictable characteristics of scene text and noise , text characters in strings are often touching that affects the performance of segmentation and recognition . This paper presents a novel approach for curved text recognition using Hidden Markov Models ( HMM ) . From curved text , a path of sliding window is estimated and features extracted from the sliding window are fed to the HMM system for recognition .
This paper presents the method based on image processing algorithms for segmentation and extraction of characters from curled text lines from document images . The algorithm performs the curved text segmentation using x-line and base line . The words in the document image are identified and bounding boxes are plotted around the words .
However , besides the quadrilateral shape , there are many other various shapes of text line in natural scene . Therefore , recent research have begun to explore curve text line detection . In this paper we explore both arbitrary - oriented and curve text detection . Our method named sliding line point regression ( SLPR ) is based on 2 - step object detection methods using Faster R - CNN or R - FCN .
To show the effectiveness of our proposed PSENet , we conduct extensive experiments on three competitive benchmark datasets including ICDAR 2015 , ICDAR 2017 MLT and SCUT - CTW1500 . Among these datasets , SCUT - CTW1500 is explicitly designed for curve text detection , and on this dataset we surpass the previous state - of - the - art result by absolute 6.37 % . Furthermore , the proposed PSENet achieves better or at least comparable performance on the ordinary quadrangular text datasets : ICDAR 2015 and ICDAR 2017
A new efficient text detection method for image spam filtering Public Scene Text Detection and Recognition The Equipment Nameplate Dataset for Scene Text Detection and Recognition∗ Text-Line Detection in Camera-Captured Document Images Using the State Estimation of Connected Components A New Method for Handwritten Scene Text Detection in Video Text extraction from scene images by character appearance and structure modeling SynthText3D: synthesizing scene text images from 3D virtual worlds Statistical part-based models: theory and applications in image similarity, object detection and region labeling DeepFlux for Skeletons in the Wild R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Shape Robust Text Detection With Progressive Scale Expansion Network EAST: An Efficient and Accurate Scene Text Detector IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection Video frames text detection through Bayesian classification and boundary growing method TextEdge: Multi-oriented Scene Text Detection via Region Segmentation and Edge Classification Multi-oriented text detection from natural scene images based on a CNN and pruning non-adjacent graph edges Detection of multi-oriented moving text in videos / Vijeta Khare Multi-Oriented and Multi-Lingual Scene Text Detection With Direct Regression Fused Text Segmentation Networks for Multi-oriented Scene Text Detection Location Sensitive Regression Algorithm for Multi-oriented Scene Text Detection with Focal Loss Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images A skeleton-based method for multi-oriented video text detection Pyrboxes: An efficient multi-scale scene text detector with feature pyramids Fused Text Segmentation Networks for Multi-oriented Scene Text Detection R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Multi-oriented text detection and verification in video frames and scene images Irregular scene text detection via attention guided border labeling Textline information extraction from grayscale camera-captured document images A Framework for Curved Videotext Detection and Extraction Ridges Based Curled Textline Region Detection from Grayscale Camera-Captured Document Images An Effective Edge and Texture Based Approach towards Curved Videotext Detection and Extraction HMM-Based Multi Oriented Text Recognition in Natural Scene Image Segmentation and extraction of text from curved text lines using image processing approach Sliding Line Point Regression for Shape Robust Scene Text Detection Shape Robust Text Detection With Progressive Scale Expansion Network
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ROBIO ICFHR 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICDAR Document Analysis Systems 2018 24th International Conference on Pattern Recognition (ICPR) ArXiv ICIP CAIP ACPR 2018 24th International Conference on Pattern Recognition (ICPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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