HANDWRITTEN CHARACTER RECOGNITION USING CNN
Keywords:
Handwritten Character Recognition, Convolutional Neural Networks (CNNs), Machine Learning, Deep Learning, Optical Character Recognition (OCR)Abstract
This project explores the development of a Handwritten Character Recognition system utilizing machine learning and deep learning techniques, specifically convolutional neural networks (CNNs). The system demonstrates high accuracy in identifying handwritten characters from optical images or direct input methods. By leveraging advanced pre-processing methods, the project highlights its practical applicability in digitizing handwritten documents, automating postal services, and enhancing accessibility tools. Future work aims to expand the dataset, improve recognition of cursive and connected characters, and integrate real-time applications. The project underscores the potential of transfer learning and other deep learning architectures to create sophisticated, versatile handwritten character recognition systems.
References
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Salvador España –Boquera : ”Continuous handwritten script recognition”, in Doermann, D.Tombre,K.(eds.): Handbook of Document Image Processing and Recognition , Springer Verlag 2020.
A. Brakensiek, J. Rottland,A.Kosmala, J.Rigoll . A new combination scheme for “HMM-based classifiersand its application to handwriting recognition”. In Proc.16th Int. Conf. on Pattern Recognition, volume 2, pages 332–337. IEEE, 2022.
