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An Application of Deep Learning in Character Recognition: An Overview

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Handbook of Deep Learning Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 136))

Abstract

For automated document analysis, OCR (Optical character recognition) is a basic building block. The robust automated document analysis system can have impact over a wider sphere of life. Many of the researchers have been working hard to build OCR systems in various languages with significant degree of accuracy, character recognition rate and minimum error rate. Deep learning is the start of art technique with efficient and accurate result as compared to other techniques. Every language, moreover every script have its own challenges e.g. scripts where characters are well separated are less challenging as compared to cursive scripts where characters are attached with one another. In this chapter, we would take a detailed account of the state of art deep learning techniques for Arabic like script, Latin script and symbolic script.

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Author information

Authors and Affiliations

  1. Govt. Girls Postgraduate College No. 1, Abbottabad, Pakistan

    Sana Saeed & Saeeda Naz

  2. University of Technology, Sydney, Australia

    Muhammad Imran Razzak

Authors
  1. Sana Saeed
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  2. Saeeda Naz
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  3. Muhammad Imran Razzak
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Corresponding author

Correspondence to Saeeda Naz .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  3. University of Canberra, Bruce, ACT, Australia

    Dharmendra Sharma

  4. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

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Saeed, S., Naz, S., Razzak, M.I. (2019). An Application of Deep Learning in Character Recognition: An Overview. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_3

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