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Amharic Text Image Recognition: Database, Algorithm, and Analysis
Dept. of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany. Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia.
Dept. of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia.
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2019 (English)In: The 15th IAPR International Conference on Document Analysis and Recognition: ICDAR 2019, IEEE, 2019, p. 1268-1273Conference paper, Published paper (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1268-1273
Series
International Conference on Document Analysis and Recognition, ISSN 1520-5363, E-ISSN 2379-2140
Keywords [en]
Amharic script, OCR, BLSTM, CTC, text image recognition
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-78688DOI: 10.1109/ICDAR.2019.00205Scopus ID: 2-s2.0-85079859423OAI: oai:DiVA.org:ltu-78688DiVA, id: diva2:1426641
Conference
The 15th IAPR International Conference on Document Analysis and Recognition (ICDAR 2019), 20-25 September, 2019, Sydney, Australia
Note

ISBN för värdpublikation: 978-1-7281-3014-9, 978-1-7281-3015-6

Available from: 2020-04-27 Created: 2020-04-27 Last updated: 2020-04-27Bibliographically approved

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Liwicki, Marcus

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