A neural network based handwritten Meitei Mayek alphabet optical character recognition system

• Published in: 2014 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 5
• Main Authors: Laishram, Romesh, Singh, Pheiroijam Bebison, Singh, Thokchom Suka Deba, Anilkumar, Sapam, Singh, Angom Umakanta
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Artificial neural networks; Character recognition; Handwriting recognition; Histogram; Histograms; Image segmentation; Meitei-Mayek; Neural Network; OCR; Optical character recognition software; Segmentation
• ISBN: 1479939749; 9781479939749
• DOI: 10.1109/ICCIC.2014.7238510

Handwritten character recognition is a part of optical character (OCR) system. OCR can be applied to both printed text and handwritten documents. In this paper we discussed the handwritten character recognition of Meitei Mayek (Manipuri script). Although OCR has been studied and developed for many Indian script very few works have been reported so far for Meitei-Mayek. This paper describes the handwritten Meitei Mayek (Manipuri script) alphabets recognition (HMMAR) using a neural network approach. The alphabet database is pre-processed and the extracted feature is sent to a neural network system for training. The trained neural network is further tested and performance analysis is observed. The emphasis is given on the process of character segmentation from a whole document i.e. isolating a single character image from a complete scanned document.