Himansh Gupta
Papers
1
Total Citations
2
H-Index
1
About
Himansh Gupta is a researcher in the field of machine learning and computer vision, with a particular focus on deep learning architectures for pattern recognition. His most-cited work, "Recognition of Handwritten Digits Using Convolutional Neural Network in Python and Comparison of Performance for Various Hidden Layers" (2023), has garnered 2 citations, demonstrating early impact in the domain of optical character recognition. In this study, Gupta systematically explores the performance of convolutional neural networks (CNNs) for handwritten digit classification, providing a comparative analysis of different hidden layer configurations. His contribution lies in offering practical insights into optimizing CNN architectures for accuracy and efficiency, which is foundational for applications in automated document processing, postal mail sorting, and assistive technologies. By benchmarking performance across varying layer depths, Gupta helps bridge the gap between theoretical deep learning models and real-world deployment. His work serves as a valuable resource for students and researchers seeking to understand the trade-offs in neural network design, particularly in resource-constrained environments. As his citation count grows, Gupta’s research continues to influence the development of robust, scalable vision systems.
Research Focus
Key Achievements
Top Papers
- 1