Sumit Srivastava
Papers
1
Total Citations
100
H-Index
1
About
Sumit Srivastava is a leading researcher in the fields of pattern recognition, deep learning, and assistive technologies, with a particular focus on handwritten character recognition for Indic scripts. His most impactful work addresses the critical challenge of recognizing handwritten Devanagari characters, a task with profound applications in assistive technology for blind and visually impaired users, human–robot interaction, and automated document processing. In his highly cited 2018 paper, Srivastava pioneered the use of layer-wise training of deep convolutional neural networks combined with adaptive gradient methods, achieving state-of-the-art accuracy on complex character sets. This work, garnering over 100 citations, has become a foundational reference for researchers working on script recognition and deep learning optimization. Srivastava’s contributions bridge the gap between advanced machine learning techniques and real-world accessibility solutions, demonstrating how robust character recognition can empower users with visual impairments and streamline human–computer interaction. His research continues to influence the development of intelligent systems that interpret diverse handwriting styles, making digital interfaces more inclusive and efficient.
Research Focus
Key Achievements
Top Papers
- 1