Publication detail

Offline Handwritten Text Recognition Using Support Vector Machines

RAJNOHA, M. BURGET, R. DUTTA, M. K. ISSAC, A.

Original Title

Offline Handwritten Text Recognition Using Support Vector Machines

English Title

Offline Handwritten Text Recognition Using Support Vector Machines

Type

conference paper

Language

en

Original Abstract

Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic. This paper describes a method for handwritten text recognition (HWR) of this font. In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier. We have trained and statistically evaluated several models, where we have focused on recognition of different styles of writing of the same characters - for the forensic purposes and identification of the author of a document. The best model has achieved 92.86 % accuracy without any further postprocessing, e.g. a spellchecker. We also proposed using more than one classification model for character recognition that has shown to increase accuracy when compared to a single model approach.

English abstract

Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic. This paper describes a method for handwritten text recognition (HWR) of this font. In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier. We have trained and statistically evaluated several models, where we have focused on recognition of different styles of writing of the same characters - for the forensic purposes and identification of the author of a document. The best model has achieved 92.86 % accuracy without any further postprocessing, e.g. a spellchecker. We also proposed using more than one classification model for character recognition that has shown to increase accuracy when compared to a single model approach.

Keywords

HWR; OCR; SVM; support vector machines; text recognition

Released

02.02.2017

ISBN

978-1-5090-2796-5

Book

2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)

Pages from

132

Pages to

136

Pages count

5

BibTex


@inproceedings{BUT133620,
  author="Martin {Rajnoha} and Radim {Burget} and Malay Kishore {Dutta} and Ashish {Issac}",
  title="Offline Handwritten Text Recognition Using Support Vector Machines",
  annote="Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic. This paper describes a method for handwritten text recognition (HWR) of this font. In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier. We have trained and statistically evaluated several models, where we have focused on recognition of different styles of writing of the same characters - for the forensic purposes and identification of the author of a document. The best model has achieved 92.86 % accuracy without any further postprocessing, e.g. a spellchecker. We also proposed using more than one classification model for character recognition that has shown to increase accuracy when compared to a single model approach.",
  booktitle="2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)",
  chapter="133620",
  howpublished="electronic, physical medium",
  year="2017",
  month="february",
  pages="132--136",
  type="conference paper"
}