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On the Development of an OCR System for Indian Postal Automation

Bag om On the Development of an OCR System for Indian Postal Automation

Various problems towards the development of Indian Postal Automation (IPA) system is discussed here. More specifically, in this endeavor, some of the tasks such as, statistical analysis of Indian postal documents, bi-lingual pin code digit recognition, script identification prior to city name recognition, and Dynamic Programming (DP) based city name recognition of Bangla script are the major contributions of this work. The feature sets considered  for the work consists of Structural and Statistical features, Normalized binary pixel images, Direction code based features, Gradient based features etc. For recognition, two categories of classifiers, viz., Multi Layer Perceptron (MLP) and Modified Quadratic Discriminant Function (MQDF) based classifiers have been selectively tested here and detailed results are presented.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783844314038
  • Indbinding:
  • Paperback
  • Sideantal:
  • 148
  • Udgivet:
  • 17. marts 2011
  • Størrelse:
  • 152x229x9 mm.
  • Vægt:
  • 227 g.
  • BLACK NOVEMBER
Leveringstid: 2-3 uger
Forventet levering: 11. december 2024

Beskrivelse af On the Development of an OCR System for Indian Postal Automation

Various problems towards the development of Indian Postal Automation (IPA) system is discussed here. More specifically, in this endeavor, some of the tasks such as, statistical analysis of Indian postal documents, bi-lingual pin code digit recognition, script identification prior to city name recognition, and Dynamic Programming (DP) based city name recognition of Bangla script are the major contributions of this work. The feature sets considered  for the work consists of Structural and Statistical features, Normalized binary pixel images, Direction code based features, Gradient based features etc. For recognition, two categories of classifiers, viz., Multi Layer Perceptron (MLP) and Modified Quadratic Discriminant Function (MQDF) based classifiers have been selectively tested here and detailed results are presented.

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