A Hybrid Approach for Handwritten Character Recognition Using Stroke Width Variation and Transformer-based Feature Encoding

Author(s)	Dr. Geetha Ramani R, Mohesh B
Country	India
Abstract	This project proposes a novel approach that combines stroke width-based seg- mentation and deep learning-based recognition for handwritten documents. The preprocessing employs a Feature-Driven Stroke Width Variation (SWV) technique for robust text segmentation. For recognition, a ResNet-based CNN extracts hierarchical features followed by a Transformer encoder to model sequential spatial dependencies. Experimental results demonstrate the effectiveness of this hybrid model in recognizing complex handwritten characters with varying stroke widths and distortions
Keywords	Handwritten Document Recognition, Stroke Width Variation (SWV), Text Segmentation, ResNet, Transformer Encoder, Feature Extraction
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-16
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4032
Short DOI	https://doi.org/g9kc8b

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology