Off-line Odia Handwritten Character Recognition: A Hybrid Approach

  • Abhisek Sethy
  • Prashanta Kumar Patra
  • Deepak Ranjan Nayak
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)


Optical character recognition (OCR) is one of the most popular and challenging topic of pattern recognition with a wide range of applications in various fields. This paper proposes an OCR system for Odia scripts which comprises of three stages, namely preprocessing, feature extraction, and classification. In the preprocessing stage, we have employed median filtering on the input image and subsequently we have applied normalization and skeletonization methods over images for extraction of boundary edge pixel points. In the feature extraction stage, initially the image is divided into 3 × 3 grids and the corresponding centroids for all the nine zones are evaluated. Thereafter, we have drawn the horizontal and vertical symmetric projection to the nearest pixel of the image which is dubbed as binary external symmetry axis constellation for unconstrained handwritten character. From which we have calculated the horizontal and vertical Euclidean distance for the same nearest pixel from centroid of each zone. Then we have calculated the mean Euclidean distance as well as the mean angular values of the zones. This is considered as the key feature values of our proposed system. Lastly, both kernel support vector machine (KSVM) and quadratic discriminant classifier (QDA) have been separately used as the classifier. To validate the proposed system, a series of experiments have been carried out on a standard database as NIT Rourkela Odia Database. From the database, we select 200 samples from each of the 47 categories. Simulation results based on a tenfold cross-validation approach indicate that the proposed system offers better recognition accuracy then other competent schemes. Moreover, the recognition accuracy obtained by KSVM and QDA classifier is 96.5 and 97.4%, respectively.


Optical character recognition Feature vector BESAC KSVM QDA 



The authors are sincerely thankful to the Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneswar. And we are also thankful to all the authors of references.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Abhisek Sethy
    • 1
  • Prashanta Kumar Patra
    • 1
  • Deepak Ranjan Nayak
    • 2
  1. 1.Department of Computer Science and EngineeringCollege of Engineering and Technology BhubaneswarBhubaneswarIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology RourkelaRourkelaIndia

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