Offline handwritten Amharic word recognition

Authors:
Yaregal Assabie;Josef Bigun
Affiliations:
Department of Computer Science, Addis Ababa University, Ethiopia;School of Information Science, Computer and Electrical Engineering, Halmstad University, Sweden
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.