An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

TL;DR

This paper proposes an efficient Hidden Markov Model approach for offline handwritten numeral recognition, aiming to improve accuracy and robustness in recognizing handwritten digits.

Contribution

It introduces a novel Hidden Markov Model tailored for offline handwritten numeral recognition, enhancing recognition efficiency and accuracy over existing methods.

Findings

Achieved higher recognition accuracy compared to traditional methods

Reduced error rates in handwritten numeral classification

Demonstrated robustness across diverse handwriting styles

Abstract

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with the