BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network
N. T. Diba N. Akter S. A. H. Chowdhury J. E. Giti






BMI Prediction from  Handwritten English Characters Using a Convolutional Neural Network





    N. T. Diba1, N. Akter2, S. A. H. Chowdhury3
Dept. of Electronics & Telecommunication Engineering 

Rajshahi University of Engineering & Technology

Rajshahi, Bangladesh

nishattasnimediba09@gmail.com1,
 nasrinmanha123@gmail.com2, arif.1968.ruet@gmail.com3
J. E. Giti4
Dept. of Electrical & Electronic Engineering 

Rajshahi University of Engineering & Technology

Rajshahi, Bangladesh

jishan.e.giti@gmail.com4

    September 9, 2024
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§ ABSTRACT 
A person’s Body Mass Index, or BMI, is the most
widely used parameter for assessing their health. 

BMI is a crucial predictor of potential diseases that may arise at higher body fat levels because it is correlated with body fat. 
Conversely, a community’s or an individual’s nutritional status can be determined using the BMI. 
Although deep learning models are used in several studies to estimate BMI from face photos and other data, no previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. 
This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN). A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task. The proposed CNN-based approach 
reports a commendable accuracy of 99.92%.
Performance comparison with other popular CNN architectures reveals that AlexNet and InceptionV3 achieve the second and third-best performance, with the accuracy of 99.69%  and 99.53%, respectively. 





Body Mass Index (BMI), Convolutional Neural Network (CNN), Deep Learning, English Handwritten Character (EHC), Forensic Science.




§ INTRODUCTION

The relationship between height and weight is calculated using the body mass index, or BMI. The simplest indicator we have for defining overweight and obesity is BMI. Many people consider BMI to be a crucial measure of health. 
An increased likelihood of cardiovascular disorders, including high blood pressure and diabetes, is shown with a progressive increase in BMI. Hence, an automated system for estimating BMI from face images or other body attributes would be very convenient for continuous monitoring of BMI to ensure physical well-being. 
Besides body attributes, handwriting can also be considered as a person-specific attribute with each individual’s writing style being distinct and personal. This paper demonstrates the automatic estimation of a person's BMI from handwritten data belonging to that individual. 
In addition to health monitoring, BMI estimation from handwriting can play a vital role in digital forensics for crime scene analysis. 

Deep learning models serve as the backbone of the automated BMI prediction system. 
Convolutional neural network (CNN), a particular kind of deep learning model inspired by the human visual system, is utilized in this work. Through convolutional (conv) layers, CNNs identify intricate patterns and characteristics of handwritten character images to estimate the BMI value of a person. 

The streamlined procedure of an automated BMI prediction system is shown in Fig. <ref>.

This paper aims to further explore the ability of CNNs
to determine BMI from a handwriting dataset. To accomplish this, we use a new
dataset called the English Handwritten Character (EHC)
dataset[ The dataset is available at  
<https://rb.gy/wdxynb>.]
and design a benchmark CNN model[
The codes will be publicly accessible at  
<https://rb.gy/xub8us>.] for this dataset.
Our contributions are summarized as follows:



  * Images, along with labels such as student name, height, weight, and BMI information, are carefully collected to ensure the uniformity and quality of the dataset. 


  * Designing a custom CNN to estimate BMI values correctly from the images of the EHC dataset.
 

  * Conducting ablation study of the proposed CNN to provide a better understanding of its learning process.
 

  * Evaluating the results of the proposed
model based on performance metrics in comparison to the prior models.








               

To the best of the authors' knowledge, this is the first work that utilizes handwriting for BMI prediction. Despite being the first work on BMI estimation from a handwriting dataset, our CNN model outperforms state-of-the-art (SOTA) models in terms of prediction accuracy. The rest of the paper is organized as follows. Section II discusses existing related work and the research gap. In Section III, we describe the details of the EHC dataset collection. Section IV provides the methodology and the design procedure of the proposed CNN. Performance evaluation of our model along with a comparison with the existing models are shown in Section V. Finally, Section VI includes the concluding remarks as well as future works.





§ RELATED WORK

CNNs have gained a lot of interest in recent years for classifying a variety of handwriting datasets for handwritten character recognition (HCR) <cit.>, writer <cit.>, and gender identification <cit.>. We briefly review research works on each task. There are some well-known handwritten text datasets named IAM <cit.>, MNIST <cit.>, EMNIST <cit.>, and Kaggle alphabet dataset<cit.> to accomplish these tasks. All of these datasets are English handwriting datasets. 



HCR:


Simard et al. <cit.> introduce a basic CNN model which afterward significantly improves HCR accuracy, offering SOTA performance <cit.>. To identify handwritten English characters, Narayan and Muthalagu <cit.> develop another CNN achieving a high real-time recognition accuracy of 97.59%.

The model implemented by Saqib et al. <cit.> outperformed prior SOTA models on the unbalanced EMNIST dataset with a peak accuracy of 99.563% while maintaining its lightweight design.

In contrast, SpinalNet <cit.> splits each layer into three splits to achieve a competitive performance compared to WaveMix <cit.>, the current SOTA HCR model on the EMNIST dataset. WaveMix blocks utilize multi-level two-dimensional discrete wavelet transform to provide scale-invariance, shift invariance, and sparseness of edges. 





Writer identification: To tackle the challenge of the writer identification task, Rehman et al. <cit.> used a deep transfer learning model called AlexNet <cit.> to identify 1017 writers on the QUWI dataset <cit.> by extracting unique features from handwritten text that included pen pressure, different inks, and other information. 
On the contrary, using words as features, Fiel and Sablatnig <cit.> trained and assessed a CNN model for writer verification, and depending on the particular words, they were able to get accuracy rates ranging from 77% to 96%. Similarly, WriterINet <cit.>, a two-stream CNN-based SOTA writer identification approach on various datasets utilizes word images to generate discriminative and deep features. 

In a separate study, Dlamini and Zyl <cit.> employ Siamese CNN networks for verification of authors using the NIST-SD19 dataset <cit.>, concentrating on single letters rather than whole words and, achieve average accuracy of 80% on unseen test data. A recurrent neural network (RNN) based model named global-context residual RNN is presented by He and Schomaker <cit.> to effectively identify a writer using sequential handwritten data. In contrast, Obaidullah et al. <cit.> propose a stack ensemble network where the truncated transfer learning models are stacked along with a shallow CNN to perform ensemble learning for signature-based verification of a writer's identity. A combination of local and global features based on VLAD descriptor <cit.> is investigated by Liang et al. <cit.> in a deep learning framework for author identification.


Gender identification: Handwriting has been used as an indication to identify gender by treating the problem as binary classification. The majority of existing approaches have relied on a few common datasets, such as IAM on-line <cit.>, QUWI <cit.>, KHATT <cit.>, and MSHD <cit.>. Using handwriting data, Illouz et al. <cit.> created a CNN-based model for gender categorization. The model has four conv layers, a dense layer, and a softmax output layer. The classifier was evaluated using the HEBIU dataset, which includes 810 handwritten samples in Hebrew and English from 405 participants. The model's maximum accuracy of 82.89% is obtained when Hebrew samples were used for training and English samples for testing. Another CNN-based model for gender and handedness classification was presented by Morera et al. <cit.>. Two publicly accessible handwriting datasets (IAM and KHATT) were used to test this model.

and achieved the highest accuracy of 80.72% for gender classification on the IAM dataset.

From the above discussion, it is observed that no prior handwriting-related work has focused on estimating a person's BMI from handwritten data. However, several deep learning-based solutions for BMI prediction from body attributes exist in the literature. 

Wen and Guo <cit.> carry out the first study to show how geometrical parameters, such as cheekbone-to-jaw width, width to upper facial height ratio, perimeter-to-area ratio, and eye size, can be used to automatically estimate BMI from facial photos. The MORPH-II dataset <cit.>, which is not publicly available, served as the basis for this work. However, VisualBMI <cit.>, Bollywood <cit.>, and VIP attributes <cit.> are the three major publicly accessible BMI-annotated facial picture datasets that are collected from social media.
For BMI inference over these three public datasets, Siddiqui et al.<cit.> apply VGG-19 <cit.>, ResNet-50 <cit.>, MobileNet-V2 <cit.>, and LightCNN-29 <cit.>. Among these models, ResNet-50 achieves a minimal mean absolute error of 1.04. 

On the other hand, Yousaf et al. <cit.> apply region-aware global average pooling (GAP) on the extracted features obtained from different facial regions using their designed models, FaceNet and VGGFace. The use of the region-aware GAP enhances the performance by 22.4%, 3.3%, and 63.09% on VIP-attribute, VisualBMI, and Bollywood datasets, respectively, compared to using a standard GAP layer. 
A new dataset called the Reddit-HWBMI dataset is introduced by Haritosh et al. <cit.> where Xception <cit.> is claimed as the best model for that dataset. Similarly, Jiang et al.<cit.> apply deep learning models to improve BMI prediction accuracy on the FIW-BMI and Morph-II datasets.


According to the above-mentioned investigations on BMI prediction and the authors' understanding, none of the previous works has estimated BMI from handwritten characters using a CNN. To address this research gap, we aim to estimate BMI values from individuals' handwritten English characters. 

This objective is achieved by collecting the EHC dataset and
designing a SOTA model for the dataset by treating the BMI prediction problem as the classification problem.

 




§ DATASET COLLECTION AND DESCRIPTION

In this section, we provide details about the collection process and characteristics of the EHC dataset obtained from the students of RUET, a prestigious engineering University.



 §.§ Dataset Collection

The EHC dataset was collected from 48 students. 

The students were provided writing supplies, including A4-sized paper and pens, and asked to write the lowercase English alphabet inside the paper's illustrated box. For further use of the dataset, each writer's personal information, including the roll number, age, height, weight, and BMI, was written at the top of the A4-sized paper. To capture the natural variation in handwritten data, the students were requested to write each character three times in their own handwriting style, without any guidelines. This was done because writing samples of the same person at different times vary in the character's writing style, shape, size, and placement. In total, 48 A4-sized paper forms were collected, resulting in a dataset comprising 26×3 characters from 48 individuals. 

Fig. <ref> illustrates some samples from this diverse handwritten English dataset.




 §.§ Dataset Description

The collected dataset provides 3744 raw photos with a
variety of lowercase English letters.
These characters were written in a variety of sizes, and
styles, which represented the writers' varied writing 

habits. The EHC dataset has undergone a comprehensive analysis of the handwritten characters that are being used to design the BMI prediction system.

This new dataset contains both challenging samples, where the characters might be written quickly or with slight distortions, as well as easier-to-read samples with clear and well-formed characters. Some students may have written certain
characters faster than others, which could introduce variations in the shape and quality of those characters. This aspect of the dataset reflects real-world scenarios where handwriting can vary due to the speed at which individuals write. As a result, this dataset offers the chance to evaluate the model’s accuracy in estimating BMI based on the quickly and slowly written lowercase English characters.




figure1



§ METHODOLOGY

CNN has revolutionized the field of computer vision and
has been widely used in various applications, including image
classification, object detection, and, in our case, BMI prediction. CNN excels at capturing local patterns and hierarchical representations in visual data, making them particularly suitable for
tasks like BMI prediction from lowercase English characters. Fig. <ref> shows the whole pipeline of the CNN-based BMI prediction approaches.



 §.§ Data Preprocessing


The pre-processing step includes all the procedures needed to create a clear character picture so that the feature extraction stage works effectively. 

Data preprocessing begins by scanning and segmenting the handwritten characters that are written on the A4-sized paper. 
During segmentation, the scanned image of the A4-sized paper containing a sequence of alphabets is divided into sub-images, each representing a single isolated character. 
However, scanned documents often contain contaminants such as dust, spots, dots, or lines, which are classified as noise. To improve estimation results significantly, it is necessary to remove the noise from the segmented images. This stage is vital as its successful execution enhances the accuracy of BMI determination and reduces misclassification. Then, we
resized the noise-free segmented images to a consistent size of 224×224 so that all images could be transformed into a uniform format for training. 








 §.§ Data Augmentation


By applying several changes to the original data, new
training samples were created, utilizing data augmentation. It
is a widely used approach in machine learning, especially when dealing with limited datasets. For our EHC dataset, which
contains only 3744 original image samples, data augmentation becomes vital to expand its volume. We applied six types of augmentations to these sample images, resulting in a total of 26,208 photos. These augmentations included increases in brightness, contrast, and sharpness, as well as the addition of Gaussian noise, Gaussian blur, and jitter. Fig. <ref> displays some samples from the augmented EHC dataset.













 §.§ Proposed Model


The proposed CNN architecture is designed as the classification model to quickly and accurately determine BMI from scanned handwritten isolated English character photos. It is created by stacking layers one after the other starting with the input layer and proceeding all through intermediate layers to the output layer.
Our base model is composed of two conv layers, each followed by a max-pooling layer. ReLU activation <cit.> is applied after each conv layer and the hidden layer, introducing non-linearity and enhancing the model’s ability to learn complex representations from higher level features. Fig. <ref> shows the interaction of input images with the proposed CNN architecture, designed to determine a writer's BMI from the handwritten character images.



 §.§ Performance Metrics

In this study, the performance metrics used to assess the model performance are accuracy, precision, recall, and, F1-score. Please note that these performance metrics reported throughout the paper are for the
testing dataset unless mentioned otherwise.



 §.§ Implementation Details

Our model is trained from scratch using an optimizer with a learning rate of 0.0001 and batch size of 32. The BMI estimation system is implemented using the Python 3.7 programming language and Keras library with TensorFlow environment. The entire experiment is performed using GPU P100 on the Kaggle Cloud Platform, a data science competition platform. The training, validation, and testing datasets are divided into around 70:15:15 

ratios. 
The training dataset comprises 18432 photos, while the remaining 7776 images have been distributed equally into testing and validation. 





§ RESULTS AND ANALYSIS

This section contains an ablation study of the proposed CNN and a comparison of our model with SOTA classification models. An ablation study provides insights into the significance of each layer’s contribution to the overall network’s effectiveness. 





 §.§ Ablation Study

An ablation study is conducted by changing the number of filters in conv layers, the number of conv layers, max-pooling layers, dense layers, and dropouts of our base model. The results, summarized in Table <ref>, show that the last and fourth rows represent the base and best models, respectively. The accuracy of our base model is 80.73%. We observed improved accuracy with slight changes to the base model configuration. The best performance is achieved with a model consisting of three conv and max-pooling layers, and three dense layers each followed by 50% dropout to prevent overfitting. our best model achieves an accuracy of 99.92% (shown as bold in Table <ref>).  


The accuracy of 99.92% indicates that out of 3888 test images of the EHC dataset, BMI is predicted accurately for 3885 character images by our best model. 
The remaining 3 images, where the model incorrectly estimates a BMI value either as belonging to a particular BMI class or not, contribute to the total false positive or false negative count, respectively.
To tackle the overfitting issue, the proposed model is trained for 100 epochs with early stopping. Each epoch involves 576 steps, meaning that batches of 32 data points each are processed 576 times, totaling 18432 training samples.  



The training and validation loss curves for our best model are shown in Fig. <ref>. As expected, the validation loss follows the training loss. Fig. <ref> also indicates that the losses stopped after approximately 49 epochs, suggesting that the training process converges around this point.



 §.§ Performance Comparison with Related Works

We evaluate the performance of our model in comparison to several popular SOTA classification models. The objective of the comparison is to assess the model's ability to identify a person's BMI from handwritten characters of the EHC dataset. Based on this comparison, we discovered that our model performs better than the widely used classification models in terms of accuracy, precision, recall, and F1-score. The comparative findings are shown in Table <ref>.

Since the lack of previous research on BMI estimation from handwritten characters, the proposed CNN's simplicity and small dimensions have been beneficial—especially in view of the small size of our dataset. Our model performed better than other popular classification networks, mostly due to its ability to extract meaningful characteristics from the relatively small dataset. 
Our model outperforms AlexNet <cit.>, InceptionV3 <cit.>, DenseNet121 <cit.>, ResNet50 <cit.>, EfficientNetb0 <cit.>, Xception <cit.>, and MobileNetV2 <cit.> models by 0.23%, 0.39%, 0.41%, 0.62%, 0.65%, 0.93% and 1.85%, respectively, in terms of relative improvement on the prediction accuracy.





§ CONCLUSION AND FUTURE WORK

This paper attempts to tackle a new task of estimating BMI from images of English handwritten characters. 
Our CNN-based approach to tackle this task not only enhances the accuracy of BMI estimation but also surpasses the state-of-the-art solutions for BMI determination, setting a new benchmark in a field that has been scarcely addressed in the literature. 
In the future, we intend to add new features to extend the functionality of our model. Future extension includes addressing the difficulties presented by other difficult languages like Bengali, Arabic, Japanese, Chinese, Korean, Finnish, as well as multilingual scripts for BMI estimation. We also plan to create an Android app that takes a photo of the handwriting and instantly determines the BMI from it.











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§ ACKNOWLEDGMENT

The authors would like to acknowledge the contribution of all participating students of Rajshahi University of Engineering & Technology (RUET), Bangladesh for providing their handwriting, height, weight, and BMI values.

The authors would also like to thank Rifa Tabassum Mim and Naila Noyari Islam of RUET for their help with the collection of the EHC dataset.


10

url@samestyle








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