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UHTelPCC: A Dataset for Telugu Printed Character Recognition

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

Abstract

This paper describes how UHTelPCC, a dataset for Telugu printed character recognition, is created and its characteristics. The dataset is created from characters extracted from images of printed Telugu texts from the period 1950–1990. Thus, it is hoped that the dataset provides the basis for developing practical Telugu OCR systems. UHTelPCC is to provide a standard benchmark for comparing different algorithms for Telugu OCR and helps in research and development of Telugu OCR systems. UHTelPCC contains 70K samples of 325 classes, and these samples are divided into 50K, 10K, 10K training, validation, and test sets respectively. It is hoped that UHTelPCC serves like MNIST, a dataset for handwritten digit recognition, for Telugu printed character recognition. The baseline performances on the test set using KNN, MLP, and CNN are 98.85%, 99.52%, and 99.68% respectively. UHTelPCC is available at http://scis.uohyd.ac.in/~chakcs/UHTelPCC.html.

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Acknowledgment

We thank Amit Patel for his efforts in labeling connected components. The first author acknowledges the financial support received from the Council of Scientific and Industrial Research (CSIR), Government of India in the form of a Junior Research Fellowship.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Rakesh Kummari & Chakravarthy Bhagvati

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  1. Rakesh Kummari
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  2. Chakravarthy Bhagvati
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Correspondence to Rakesh Kummari or Chakravarthy Bhagvati .

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Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Kummari, R., Bhagvati, C. (2019). UHTelPCC: A Dataset for Telugu Printed Character Recognition. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_3

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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