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Recognition of handwritten documents facilitates efficient data management and enhances accessibility in areas such as archiving, finance, and healthcare. This research focuses on utilizing Convolutional Neural Networks (CNNs) for the recognition of handwritten English characters and words. A dataset of handwritten images is employed for training and evaluation. The proposed framework addresses challenges arising from variations in individual handwriting styles by leveraging CNNs to accurately identify handwritten characters and words. Experimental results demonstrate an accuracy of 94.36% for uppercase characters and 88.97% for lowercase characters. Furthermore, the model achieves 92.36% accuracy in recognizing uppercase words and 98.48% accuracy in recognizing lowercase words, highlighting the effectiveness of CNN-based approaches in handwritten text recognition.
Keywords:
Handwritten Recognition, CNN, OCR, Deep Learning, Image Processing
Cite Article:
"A CNN-BASED HANDWRITTEN ENGLISH CHARACTER RECOGNITION", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c340-c344, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604315.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator