  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.
