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automated extraction of text from images, aims to digitize and make accessible vast amounts of textual data, including historical documents and scene text.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
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