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A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts

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Abstract

The cultural and regional diversity across the world and specifically in India has given birth to a large number of writing systems and scripts having a variety of character sets. For scripts having a larger character set, just a simple keyboard with limited character set is not the optimal way for providing inputs to the computer. Variations in individual handwriting due to mood swings, changes in medium of writing, changes in writing styles, etc. pose a challenge before the character recognition (CR) research community. Similar kinds of symbols in various scripts and languages act as a big barrier in multilingual CR. Lack of benchmark results and corpora for multilingual CR hinder the research in multilingual CR. There have been only a limited number of articles for optimal combination of features and classifiers to process multilingual data. Multilingual CR has least explored the Indic scripts. This paper presents a detailed review and analysis of the work done in multilingual online as well as offline CR for Indic and non-Indic scripts. The paper mainly contributes in two ways: Firstly, it provides a clear perspective about various phases of monolingual and multilingual CR; and secondly, identifies the major deficiencies in monolingual and multilingual CR for printed and handwritten text. It contributes by giving an in-depth view of work done at each phase including data acquisition, pre-processing, segmentation, feature extraction, recognition and post-processing of CR. Issues to be resolved at each phase have also been elaborated. The recent work done using Deep and Shallow architectures has been analysed. Tools used for these architectures have been compared to highlight their pros and cons. The present work also suggests how further research can be conducted in the field of monolingual and multilingual CR. The problems such as CR in hybrid documents, identifying more reliable features, resolving issues of similar characters, identifying optimal combination strategies for deep and shallow architectures, etc. need to be tackled in future research.

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Acknowledgements

This research was supported by Council of Scientific and Industrial Research (CSIR) funded by the Ministry of Science and Technology (09/677(0031)/2018/EMR-I) as well as the Government of India.

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    Sukhandeep Kaur, Seema Bawa & Ravinder Kumar

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Kaur, S., Bawa, S. & Kumar, R. A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts. Artif Intell Rev 53, 1813–1872 (2020). https://doi.org/10.1007/s10462-019-09720-9

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