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Bilingual Documents Text Lines Extraction Using Conditional GANs

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Machine Learning and Big Data Analytics (ICMLBDA 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 401))

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Abstract

Text line extraction in document recognition is the major step. A number of classical approaches are available like projection profile, bounding box analysis, etc. These classical approaches are unable to segment the text with large variations in individual handwriting. Furthermore, segmentation of documents having data from multiple scripts creates more hurdles due to the presence of different writing styles. The usage of deep networks has been less explored in this domain due to the need of high training time and data. In this research, we have used conditional generative adversarial networks (GANs) for text line extraction in bilingual documents containing Gurumukhi-Latin scripts. It considers text line segmentation problem as image-to-image translation task. Two kinds of encoder–decoder networks are used for comparison, i.e., with skip connections and without skip connections. Dataset for bilingual handwritten documents containing 150 document images has been designed. It includes large variability in writing style and content. Results on the designed dataset for text line extraction are efficient for encoder–decoder network with skip connections.

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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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Authors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Sukhandeep Kaur, Seema Bawa & Ravinder Kumar

  2. Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

Authors
  1. Sukhandeep Kaur
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  2. Seema Bawa
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  3. Ravinder Kumar
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  4. Munish Kumar
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Rajiv Misra

  2. Cardiff University, Cardiff, UK

    Rana Omer

  3. Dept. of EE Engineering, University of London, London, UK

    Muttukrishnan Rajarajan

  4. Dept. of ECE, National University of Singapore, Singapore, Singapore

    Bharadwaj Veeravalli

  5. Dept. of Computer Science, Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  6. Dept. of CSE, Indian Institute of Information Technology, Kota, Jawahar Lal Nehru Marg, Rajasthan, India

    Priyanka Mishra

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Kaur, S., Bawa, S., Kumar, R., Kumar, M. (2023). Bilingual Documents Text Lines Extraction Using Conditional GANs. In: Misra, R., Omer, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Mishra, P. (eds) Machine Learning and Big Data Analytics. ICMLBDA 2022. Springer Proceedings in Mathematics & Statistics, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-031-15175-0_5

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