手写德文汉字识别的梯度局部自相关

2014 International Conference on High Performance Computing and Applications (ICHPCA) Pub Date : 2014-12-01 DOI:10.1109/ICHPCA.2014.7045339

Mahesh Jangid, S. Srivastava

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引用次数: 9

摘要

本文主要研究目标检测算法GLAC (Gradient Local Auto-Correlation，梯度局部自相关)在手写字符识别中的应用。除了GLAC在图像中人、行人检测和图像补丁匹配等目标检测问题上取得了比HOG和SIFT更好的效果外，HOG和SIFT已经应用于该(HCR)领域。本文利用GLAC算法对手写体德文汉字进行识别。GLAC应用于两个手写Devanagari数据库，ISIDCHAR和V2DMDCHAR。同时对数据库图像进行了保留宽高比和不保留宽高比的归一化处理。使用GLAC方法和SVM分类器，在ISIDCHAR和V2DMDCHAR上获得的最佳识别率分别为93.21%和95.21%，证明了GLAC算法用于字符识别问题的合理性。

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Gradient Local Auto-Correlation for handwritten Devanagari character recognition

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.

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

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