Gradient Local Auto-Correlation for handwritten Devanagari character recognition

Author

Jangid, Mahesh ; Srivastava, Sumit

Author_Institution

Dept. of Comput. Sci. & Eng., Manipal Univ. Jaipur, Jaipur, India

fYear

2014

Firstpage

1

Lastpage

5

Abstract

This manuscript is focus on the utilization of object detection algorithm GLAC (Gradient Local Auto-Correlation) for the handwritten character recognition (HCR) problem. HOG and SIFT are already used in this (HCR) field except GLAC which produced good results than HOG and SIFT for object detection problem like human in images, pedestrian detection and image patch matching. This paper utilized GLAC algorithm to recognize the handwritten Devanagari characters. GLAC applied on two handwritten Devanagari databases, ISIDCHAR and V2DMDCHAR. The images of databases are also normalized with and without preserving aspect ratio. Using GLAC method and SVM classifier, the best results obtained on ISIDCHAR and V2DMDCHAR are 93.21%, 95.21 % respectively that justified the utilization of GLAC algorithm for character recognition problem.

Keywords

gradient methods; handwritten character recognition; image classification; natural language processing; object detection; support vector machines; transforms; visual databases; GLAC algorithm; HCR field; HOG; ISIDCHAR; SIFT; SVM classifier; V2DMDCHAR; gradient local autocorrelation; handwritten Devanagari character recognition; handwritten Devanagari databases; image patch matching; object detection algorithm; pedestrian detection; Accuracy; Databases; Image recognition; Optical imaging; Support vector machine classification; Vectors; Devanagari; Gradient Local Auto-correlation; Handwritten recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

High Performance Computing and Applications (ICHPCA), 2014 International Conference on

Print_ISBN

978-1-4799-5957-0

Type

conf

DOI

10.1109/ICHPCA.2014.7045339

Filename

7045339