A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing
This addresses accuracy issues in digitizing books and documents for applications like education and archiving, but it is incremental as it builds on existing OCR and NLP methods.
The paper tackles the problem of low accuracy in OCR for printed textbooks and handwritten texts by proposing a post-processing pipeline using NLP tools, resulting in improved accuracy.
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.