Dujan Albaqal
Papers
1
Total Citations
4
H-Index
1
About
Dujan Albaqal is a researcher focused on the intersection of computer vision and deep learning, with a particular emphasis on optical character recognition (OCR) technologies. Their most cited work, "Implementation of OCR using Convolutional Neural Network (CNN): A Survey" (2022), provides a comprehensive overview of how convolutional neural networks are revolutionizing character recognition systems. This survey synthesizes recent advances in transforming printed materials into machine-readable text, addressing key challenges in accuracy and efficiency. With 4 citations, this work has already begun to influence researchers exploring deep learning applications in document digitization and automated data entry. Albaqal's contributions are particularly relevant as industries increasingly demand robust OCR solutions for processing historical documents, forms, and multilingual texts. Their research bridges the gap between traditional image processing and modern neural network architectures, offering practical insights for implementing CNN-based OCR systems. As the field of computer vision continues to evolve, Albaqal's work serves as a valuable resource for students and practitioners seeking to understand the current state and future directions of deep learning in character recognition.
Research Focus
Key Achievements
Top Papers
- 1Implementation of OCR using Convolutional Neural Network (CNN): A Survey4 citations · 2022