Research Article
A Recognition System for Devanagari Handwritten Digits Using CNN
Nawaraj Ghimire*
Issue:
Volume 8, Issue 2, December 2024
Pages:
21-30
Received:
20 March 2024
Accepted:
7 April 2024
Published:
29 July 2024
Abstract: A Recognition System for Devanagari Handwritten Digits using CNN, a novel approach to recognizing transcribed digits in the Devanagari script using Convolutional Neural Networks (CNN). This framework represents a significant contribution to the field of pattern recognition and language processing objective of the research project is to perform a literature review, identify an algorithm for a digits recognition system implement the Devanagari digits recognition system for educational activities. In the first phase, a dataset of 150 transcribed digit images is curated, allocating 75% for training (113 images) and 25% for validation (37 images). A Convolutional Neural Network (CNN) is designed with five convolutional layers, each utilizing 3 × 3 filters with 16, 32, 64, 128, and 128 feature maps, respectively. The experiments conducted involve varying the number of epochs, with results captured at 5, 10, 20, and 100 epochs. This comprehensive evaluation aims to understand the model's convergence and performance over different training durations. The outcomes of this phase contribute to the fine-tuning and optimization of the model for subsequent phases. In the second phase, the dataset is expanded to 100*10 (1000) images, each resized to 28 × 28 pixels through cropping. The CNN architecture remains consistent, with the previously determined layer configuration. Similar experiments are conducted, assessing the model's performance over 5, 10, 20, and 100 epochs. This model with a data size of 1000 demonstrates superior accuracy (100% on mini-batches) compared to the 150 model, with consistently high validation accuracy, while both models exhibit decreasing trends in mini-batch and validation losses, favoring the larger dataset, and maintaining a constant learning rate at 0.0100, albeit with a slightly longer time elapsed for each epoch due to the increased data size. 98.37398 accuracy in the phase 2 experiment in 100 epochs. Similar research and contributions and Devanagari’s character and word recognition system.
Abstract: A Recognition System for Devanagari Handwritten Digits using CNN, a novel approach to recognizing transcribed digits in the Devanagari script using Convolutional Neural Networks (CNN). This framework represents a significant contribution to the field of pattern recognition and language processing objective of the research project is to perform a li...
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Research Article
Bangla Optical Character Recognition for Mobile Platforms: A Comprehensive Cross-Platform Approach
Sabrina Sharmin*,
Tasauf Mim,
Mohammad Mizanur Rahman
Issue:
Volume 8, Issue 2, December 2024
Pages:
31-42
Received:
11 July 2024
Accepted:
2 August 2024
Published:
6 September 2024
Abstract: The development of Optical Character Recognition (OCR) systems for Bangla script has been an area of active research since the 1980s. This study presents a comprehensive analysis and development of a cross-platform mobile application for Bangla OCR, leveraging the Tesseract OCR engine. The primary objective is to enhance the recognition accuracy of Bangla characters, achieving rates between 90% and 99%. The application is designed to facilitate the automatic extraction of text from images selected from the device's photo library, promoting the preservation and accessibility of Bangla language materials. This paper discusses the methodology, including the preparation of training datasets, preprocessing steps, and the integration of the Tesseract OCR engine within a Dart programming environment for cross-platform functionality. This integration provides that the application could be introduced on mobile platforms without substantial alterations. The results demonstrate significant improvements in recognition accuracy, making this application a valuable tool for various practical applications such as data entry for printed Bengali documents, automatic recognition of Bangla number plates, and the digital archiving of vintage Bangla books. These improvements are crucial to further enhance the usability and reliability of Bangla OCR on mobile devices. Our cross-platform method for Bangla OCR on mobile devices provides a strong solution with exceptional identification accuracy, which helps in preserving and making Bangla language information accessible in digital format. This study has significant implications for future research and advancement in the field of optical character recognition (OCR) for intricate writing systems, especially in mobile settings.
Abstract: The development of Optical Character Recognition (OCR) systems for Bangla script has been an area of active research since the 1980s. This study presents a comprehensive analysis and development of a cross-platform mobile application for Bangla OCR, leveraging the Tesseract OCR engine. The primary objective is to enhance the recognition accuracy of...
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