A CNN-KNN Based Recognition of Online Handwritten Symbols within Physics Expressions Using Contour-Based Bounding Box (CBBS) Segmentation Technique
- 1 Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India
- 2 Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India
Abstract
The task of recognizing symbols poses a significant challenge owing to the wide variability in human handwriting. Complexity in terms of the structural representation of symbols used in physics expressions is a major challenge in the recognition process The emergence of online handwriting, fueled by the widespread adoption of handheld digital devices, particularly in educational contexts, highlights the critical importance of precise symbol recognition, especially in the teaching and learning process. In contemporary literature, there is a notable emphasis on LaTex sequencing, symbol recognition and parsing. However, deep learning continues to yield promising results in this domain. The convenience of user input provides benefits to e-learning applications. In this study, we propose three approaches for the recognition of physics symbols within physics expressions (1) A proposed Java user interface for taking input from the user, as convenience of user input provides benefits to e-learning applications. (2) Contour-based bounding box segmentation algorithm, which deals with broken symbols within physics expressions. (3) For recognition, we propose a Convolution Neural Network-K-Nearest Neighbor (CNN-KNN) recognition model, as CNN plays an important role in extracting features, which are further provided as input to the K-NN classifier using the dropout method. Combining these three approaches into a symbol recognition model provides state-of-arts results. Handwritten physics symbols were collected from 20 different writers and each writer has written 5 types of physics expressions under different categories like electric flux, Maxwell’s equations, inductance and pointing vector and moment of Interia. There were 25 classes identified from the 780 samples collected from the users. The recognition rate is identified using (1) Using CNN model, which shows an accuracy of 91.48 and (2) Using the proposed hybrid CNN-KNN model the accuracy reported is 98.06.
DOI: https://doi.org/10.3844/jcssp.2024.783.792
Copyright: © 2024 Ujwala Kolte, Sachin Naik and Vidya Kumbhar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- CNN
- Contour Based Bounding Box Segmentation (CBBS)
- K-NN
- Physics Expression
- Symbol Recognition