Open AccessEnhancing Predictability of Handwritten Document Content using HTR and Word Substitution
Author(s) -
Varshini Prakash,
K L Bhanu Moorthy,
Jasmin T. Jose
Publication year - 2020
Publication title -
international journal of innovative science and modern engineering
Language(s) - English
Resource type - Journals
ISSN - 2319-6386
DOI - 10.35940/ijisme.g1240.056720
Subject(s) - computer science , word (group theory) , artificial intelligence , natural language processing , optical character recognition , speech recognition , task (project management) , security token , metric (unit) , character (mathematics) , substitution (logic) , set (abstract data type) , pattern recognition (psychology) , similarity (geometry) , image (mathematics) , mathematics , operations management , geometry , management , computer security , economics , programming language
Handwritten Text Recognition (HTR) can become progressively abysmal when the documents are damaged with smudges, blemishes and blurs. Recognition of such documents is a challenging task. We, therefore propose a system to identify textual handwritten content in documents where the state-of-the-art Optical Character Recognition (OCR) existing at its full extent performs with low accuracy. By introducing word substitution using character and distance analysis for spell checking and word completion in such areas for giving out more accurate results using a word corpus, we improved our prediction results especially in cases where the OCR is prone to predict false positives on the smudge areas predominantly. Blur detection on every word before segmentation is also substituted with a new word by our OCR algorithm to avoid false positive results and are instead substituted with suitable words. This methodology is far more convenient and reliable since even state-of-the-art HTR technologies do not have more than 71% accuracy. The accuracy of the predicted test is measured using the text similarity metric - Fuzzy Token Set Ratio (FTSR).