Handwritten Character Recognition of Popular South Indian Scripts

  • Umapada Pal
  • Nabin Sharma
  • Tetsushi Wakabayashi
  • Fumitaka Kimura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4768)


India is a multi-lingual, multi-script country. Considerably less work has been done towards handwritten character recognition of Indian languages than for other languages. In this paper we propose a quadratic classifier based scheme for the recognition of off-line handwritten characters of three popular south Indian scripts: Kannada, Telugu, and Tamil. The features used here are mainly obtained from the directional information. For feature computation, the bounding box of a character is segmented into blocks, and the directional features are computed in each block. These blocks are then down-sampled by a Gaussian filter, and the features obtained from the down-sampled blocks are fed to a modified quadratic classifier for recognition. Here, we used two sets of features. We used 64-dimensional features for high speed recognition and 400-dimensional features for high accuracy recognition. A five-fold cross validation technique was used for result computation, and we obtained 90.34%, 90.90%, and 96.73% accuracy rates from Kannada, Telugu, and Tamil characters, respectively, from 400 dimensional features.


Character Recognition Multi Layer Perceptron Zernike Moment Chain Code Handwritten Character 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Umapada Pal
    • 1
  • Nabin Sharma
    • 1
  • Tetsushi Wakabayashi
    • 2
  • Fumitaka Kimura
    • 2
  1. 1.Computer Vision Pattern Recognition Unit Indian Statistical InstituteKolkataIndia
  2. 2.Graduate School of EngineeringMie UniversityKurimamachiya-choJapan

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