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An Overview of Handwritten Character Recognition Systems for Historical Documents

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Rediscovering Heritage Through Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 859))

Abstract

Analysing Handwritten Documents is a challenging task. This particular area cannot always come up with general solutions, given that most handwritten manuscripts contain unique characteristics that describe how the document was written, which include different handwritings. These challenges in transcribing different handwriting styles are due to various scribes contributing to the transcription of the text and degradation of the script. In this chapter, an overview of different techniques used in handwritten text recognition systems is presented. The approaches and algorithms can be adopted for different document types irrespective of the state of the scanned documents. Moreover, two different general approaches to handwritten character recognition are shown. The first approach goes through a fairly standard process to normalise, segment and recognise characters. The other approach is a segmentation free approach that uses neural networks for both segmentation and recognition.

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Notes

  1. 1.

    Binarisation [38].

  2. 2.

    Erosion and dilation techniques [45].

  3. 3.

    Machine learning [35].

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Authors and Affiliations

  1. University of Malta, Msida, Malta

    Mark Bugeja, Alexiei Dingli & Dylan Seychell

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  1. Mark Bugeja
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  2. Alexiei Dingli
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  3. Dylan Seychell
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Correspondence to Alexiei Dingli .

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Editors and Affiliations

  1. Faculty of ICT, Department of Artificial Intelligence, University of Malta, Msida, Malta

    Dylan Seychell

  2. Faculty of ICT, Department of Artificial Intelligence, University of Malta, Msida, Malta

    Alexiei Dingli

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Bugeja, M., Dingli, A., Seychell, D. (2020). An Overview of Handwritten Character Recognition Systems for Historical Documents. In: Seychell, D., Dingli, A. (eds) Rediscovering Heritage Through Technology. Studies in Computational Intelligence, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-030-36107-5_1

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