Character Recognition
Character recognition, the automated extraction of text from images, aims to digitize and make accessible vast amounts of textual data, including historical documents and scene text. Current research heavily utilizes deep learning models, particularly transformer-based architectures and convolutional neural networks, often incorporating techniques like contrastive learning and multi-modal approaches to improve accuracy and efficiency across diverse languages and document types. This field is crucial for applications ranging from document digitization and information retrieval to cultural preservation and intelligent traffic systems, driving advancements in both computer vision and natural language processing.
Papers
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study
Yulin Fei, Yuhui Gao, Xingyuan Xian, Xiaojin Zhang, Tao Wu, Wei Chen
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions
Yash Agrawal, Srinidhi Balasubramanian, Rahul Meena, Rohail Alam, Himanshu Malviya, Rohini P
Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma
Nouar AlDahoul, Myles Joshua Toledo Tan, Raghava Reddy Tera, Hezerul Abdul Karim, Chee How Lim, Manish Kumar Mishra, Yasir Zaki
Enhancement of text recognition for hanja handwritten documents of Ancient Korea
Joonmo Ahna, Taehong Jang, Quan Fengnyu, Hyungil Lee, Jaehyuk Lee, Sojung Lucia Kim
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach
Valfride Nascimento, Rayson Laroca, Rafael O. Ribeiro, William Robson Schwartz, David Menotti
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting
Alloy Das, Sanket Biswas, Umapada Pal, Josep Lladós, Saumik Bhattacharya