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Detecting Handwritten Bengali Mathematical Expressions Using Neural Networks

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dc.contributor.author Ahsan, Md. Monjurul
dc.contributor.author Ahmad, Ishan
dc.date.accessioned 2025-09-14T06:01:44Z
dc.date.available 2025-09-14T06:01:44Z
dc.date.issued 2024-07-13
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14451
dc.description Project report en_US
dc.description.abstract The problem of handwritten numerical expression recognition in languages such as Bengali is not well studied in machine learning, even though research on handwritten equations is well known. This work presents a thorough method for correctly identifying and analyzing handwritten Bengali mathematical statements using deep learning. Handwritten numerals and operators in Bengali are complicated, therefore models like custom CNN Layer, ResNet34, LetNet5, Xception and Vgg19. To guarantee robustness and dependability, the system's performance is assessed using measures including accuracy, precision, recall, and F1-score. The objective is to further the field of optical character recognition (OCR) for mathematical material in Bengali by creating an accurate and efficient recognition system. en_US
dc.description.sponsorship DIU en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Optical character recognition (OCR) en_US
dc.subject Deep Learning en_US
dc.subject Pattern recognition en_US
dc.subject Natural language processing (NLP) en_US
dc.title Detecting Handwritten Bengali Mathematical Expressions Using Neural Networks en_US
dc.type Other en_US


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