2023/07/09 by Aishik Rakshit, Rakshit, Aishik, Samyak Mehta +3 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Handwritten Text Recognition Techniques #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.2307.04245
openalex publication_date 2023/07/09 · openalex created_date 2023/07/12 · openalex updated_date 2026/07/28
Optical Character Recognition (OCR) technology finds applications in digitizing books and unstructured documents, along with applications in other domains such as mobility statistics, law enforcement, traffic, security systems, etc. The state-of-the-art methods work well with the OCR with printed text on license plates, shop names, etc. However, applications such as printed textbooks and handwritten texts have limited accuracy with existing techniques. The reason may be attributed to similar-looking characters and variations in handwritten characters. Since these issues are challenging to address with OCR technologies exclusively, we propose a post-processing approach using Natural Language Processing (NLP) tools. This work presents an end-to-end pipeline that first performs OCR on the handwritten or printed text and then improves its accuracy using NLP.