Recognition of handwritten Amharic bank checks using Markov models

Amharic, a working language in Ethiopia, has its own writing system which is totally different from that of the Latin alphabet based languages. Amharic handwriting recognition is challenging due to the huge number of symbols, significant interclass similarity and also intra-class variability. The focus of this research is to investigate the possibilities of recognizing the legal amount field of handwritten Amharic bank checks. To this end, two different Markov models are investigated. The first is the so called Pseudo 2-D Hidden Markov Models (PHMM) while the second is Hidden Markov Random Field (HMRF). Different features are extracted for respective models which suits their implementation strategy. A new feature extraction technique is used which tries to extract natural features as perceived by human beings. The features extracted by this technique show a significant performance improvement.