Amharic Text Image Recognition: Database, Algorithm, and Analysis

Abstract

This paper introduces a dataset for an exotic, but very interesting script, Amharic. Amharic follows a unique syllabic writing system which uses 33 consonant characters with their 7 vowels variants of each. Some labialized characters derived by adding diacritical marks on consonants and or removing part of it. These associated diacritics on consonant characters are relatively smaller in size and challenging to distinguish the derived (vowel and labialized) characters. In this paper we tackle the problem of Amharic text-line image recognition. In this work, we propose a recurrent neural network based method to recognize Amharic text-line images. The proposed method uses Long Short Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification). Furthermore, in order to overcome the lack of annotated data, we introduce a new dataset that contains 337,332 Amharic text-line images which is made freely available at http://www.dfki.uni-kl.de/~belay/. The performance of the proposed Amharic OCR model is tested by both printed and synthetically generated datasets, and promising results are obtained.