An Implementation of Neural Network Approach for Recognition of Handwritten Odia Text

  • Sachikanta DashEmail author
  • Rajendra Kumar Das
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


Although no of arguments available that handwriting can be mimicked or forged, there is a certain level of individuality and uniqueness where the forge is not possible. As on recent studies, the handwritten character recognition (HCR) gaining more importance due to the increase in computational work. Various methods proposed for the identification of written characters. Selection of a relevant feature is probably the essential factor in achieving high recognition performance with much better accuracy in HCR system. Another critical challenge is that there was very less number of research done on Odia character. In this research work, we have used a multilayer feed-forward network with backpropagation for the recognition. Also, a few comparisons we have done on this scenario. In pre-processing, we have applied some basic algorithms for de-noising, segmentation, normalizing of characters, etc.


Feature extraction Multilayer feed-forward network Handwritten Odia character recognition Back propagation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of CSEDRIEMS Autonomous Engineering CollegeTangi, CuttackIndia
  2. 2.Department of ENTCDRIEMS Autonomous Engineering CollegeTangi, CuttackIndia

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