A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing

TL;DR

This paper introduces an end-to-end pipeline that enhances OCR accuracy for handwritten and printed texts by applying NLP-based post-processing techniques, addressing limitations of existing OCR methods in complex scenarios.

Contribution

The work presents a novel pipeline combining OCR with NLP post-processing to improve recognition accuracy in challenging handwritten and printed texts.

Findings

Improved OCR accuracy on handwritten texts.

Effective post-processing with NLP reduces recognition errors.

Pipeline outperforms traditional OCR methods in complex cases.

Abstract

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.