Post Processing of Optically Recognized Text using First Order Hidden Markov Model
Spandana Malreddy · Digital Scholarship - UNLV (University of Nevada Reno) · 2020
In this thesis, we report on our design and implementation of a post processing system for Optically Recognized text. The system is based on first order Hidden Markov Model (HMM). The Maximum Likelihood algorithm is used to train the system with over 150 thousand characters. The system is also tested on a file containing 5688 characters. The percentage of errors detected and corrected is 11.76% with a recall of 10.16% and precision of 100%