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Language-Based Classification of Document Images Using Hybrid Texture Features

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Advances in Biometrics

Abstract

Document image understanding has attracted the research community since two and half decades. Multilingual documents in the database require an automatic classification technique for browsing and sorting. This chapter provides an introduction to language-based classification of documents and review about the methods used for language detection. This chapter also proposes a segmentation-free technique for classification of document images based on the language used. A hybrid texture feature-extraction scheme using stationary wavelet transform (SWT) and histogram of oriented gradients (HOG) is presented. The multi-class support vector machine (SVM) is employed for classification of documents. The presented method is investigated on a database of 1006 document images consisting of Kannada, Telugu, Marathi, Hindi, and English language. It has shown better results compared with existing techniques. An average detection rate of 87.02% is obtained using the proposed method.

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Author information

Authors and Affiliations

  1. Department of Electronics & Communication Engineering, B.L.D.E.A’s, V.P. Dr. P.G. Halakatti College of Engineering & Technology, Vijayapur, Karnataka, India

    Umesh D. Dixit

  2. Department of Computer Science & Engineering, B.L.D.E.A’s, V.P. Dr. P.G. Halakatti College of Engineering & Technology, Vijayapur, Karnataka, India

    M. S. Shirdhonkar

Authors
  1. Umesh D. Dixit
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  2. M. S. Shirdhonkar
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Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Myanmar Institute of Information Technology, Mandalay, Myanmar

    G.R. Sinha

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Dixit, U.D., Shirdhonkar, M.S. (2019). Language-Based Classification of Document Images Using Hybrid Texture Features. In: Sinha, G. (eds) Advances in Biometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-30436-2_9

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