Inclusive Digit Recognition System
This paper presents a character recognition framework utilizing a Convolutional Neural Network (CNN) to achieve accurate and efficient classification of character images. The process begins with the acquisition of character images, followed by dataset augmentation to enhance variability and robustness. Pre-processing techniques, including normalization and resizing, are applied to both training and testing datasets to ensure uniform input quality. The augmented training set is used to develop and train the CNN model, which is then evaluated using the testing dataset. Additionally, the trained model is capable of processing real-time input for practical applications such as optical character recognition (OCR) and handwritten text recognition. Experimental results demonstrate that the proposed CNN-based approach effectively identifies characters with high accuracy, highlighting its potential for deployment in automated text recognition systems.
"Inclusive Digit Recognition System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b338-b349, September-2025, Available :https://ijsdr.org/papers/IJSDR2509142.pdf
Volume 10
Issue 9,
September-2025
Pages : b338-b349
Paper Reg. ID: IJSDR_305041
Published Paper Id: IJSDR2509142
Downloads: 00048
Research Area: Science All
Country: Chandrapur, 20, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
