BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network

• URL: http://arxiv.org/abs/2409.02584v1
• Date: Wed, 4 Sep 2024 10:06:42 GMT
• Title: BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network
• Authors: N. T. Diba, N. Akter, S. A. H. Chowdhury, J. E. Giti,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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