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IndicSTR12: A Dataset for Indic Scene Text Recognition

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Document Analysis and Recognition – ICDAR 2023 Workshops (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14193))

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Abstract

The importance of Scene Text Recognition (STR) in today’s increasingly digital world cannot be overstated. Given the significance of STR, data-intensive deep learning approaches that auto-learn feature mappings have primarily driven the development of STR solutions. Several benchmark datasets and substantial work on deep learning models are available for Latin languages to meet this need. On more complex, syntactically and semantically, Indian languages spoken and read by 1.3 billion people, there is less work and datasets available. This paper aims to address the Indian space’s lack of a comprehensive dataset by proposing the largest and most comprehensive real dataset - IndicSTR12 - and benchmarking STR performance on 12 major Indian languages (Assamese, Bengali, Odia, Marathi, Hindi, Kannada, Urdu, Telugu, Malayalam, Tamil, Gujarati, and Punjabi). A few works have addressed the same issue, but to the best of our knowledge, they focused on a small number of Indian languages. The size and complexity of the proposed dataset are comparable to those of existing Latin contemporaries, while its multilingualism will catalyse the development of robust text detection and recognition models. It was created specifically for a group of related languages with different scripts. The dataset contains over 27000 word-images gathered from various natural scenes, with over 1000 word-images for each language. Unlike previous datasets, the images cover a broader range of realistic conditions, including blur, illumination changes, occlusion, non-iconic texts, low resolution, perspective text etc. Along with the new dataset, we provide a high-performing baseline on three models: PARSeq (Latin SOTA), CRNN, and STARNet.

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Notes

  1. 1.

    Bodo, Dogri, Kashmiri, Konkani, Maithili, Nepali, and Sindhi.

  2. 2.

    Santali.

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Acknowledgement

This work is supported by MeitY, Government of India, through the NLTM-Bhashini project.

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Authors and Affiliations

  1. Centre for Vision Information Technology, International Institute of Information Technology, Hyderabad, 500032, India

    Harsh Lunia, Ajoy Mondal & C. V. Jawahar

Authors
  1. Harsh Lunia
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  2. Ajoy Mondal
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  3. C. V. Jawahar
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Corresponding author

Correspondence to Harsh Lunia .

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Editors and Affiliations

  1. University of La Rochelle, La Rochelle, France

    Mickael Coustaty

  2. Autonomous University of Barcelona, Bellaterra, Spain

    Alicia Fornés

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Lunia, H., Mondal, A., Jawahar, C.V. (2023). IndicSTR12: A Dataset for Indic Scene Text Recognition. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14193. Springer, Cham. https://doi.org/10.1007/978-3-031-41498-5_17

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  • DOI: https://doi.org/10.1007/978-3-031-41498-5_17

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