A Hybrid Approach for Handwritten Character Recognition Using Stroke Width Variation and Transformer-based Feature Encoding
Year: 2025
Type: article
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
Authors: Dr. Geetha Ramani R -, Mohesh B -
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410430810