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Handwritten Meitei Mayek recognition using three‐channel convolution neural network of gradients and gray
Author(s) -
Inunganbi Sanasam,
Choudhary Prakash,
Manglem Khumanthem
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12392
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , convolutional neural network , neocognitron , artificial neural network , grayscale , character recognition , optical character recognition , character (mathematics) , image (mathematics) , speech recognition , mathematics , geometry
The problem of searching a similar pattern is an exciting and challenging research field of pattern recognition. The intelligence of humans for vision to read is a crucial phenomenon for machine simulation and has been carried out for a few decades. Therefore, in this article, a recognition system of handwritten Meitei Mayek (Manipuri script) is introduced using a convolutional neural network. Generally, character recognition is performed using the gray scale of the image of characters. However, we have additionally considered the corresponding gradient direction and gradient magnitude images to create three‐channels image for every character so that supplementary information from gradient images can be obtained for efficient recognition. Experiments are conducted on 14 700 sample images collected from various individuals of different age groups and educational backgrounds. A recognition rate of 98.70% is obtained, which is compared with the existing methods, and it is found to be superior performance than other neural network methods on Meitei Mayek.