An improved descriptor for Chinese character recognition

Authors:
Tong Wu;Kaiyue Qi;Qi Zheng;Kai Chen;Jianbo Chen;Haibing Guan
Affiliations:
School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

Citing 8
Cited 0

Local Grayvalue Invariants for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast context histogram-An efficient discriminating local descriptor for object recognition and image matching

Pattern Recognition
Character-SIFT: A Novel Feature for Offline Handwritten Chinese Character Recognition

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The reference [1] presents a novel approach for Chinese character recognition. Based on it, we know that we can treat character recognition as an image matching problem. Compared with traditional OCR, the new approach for character recognition uniquely uses local invariant descriptors as a new feature extraction method. In this paper, we present a new local descriptor which combines the scale-invariant feature descriptor with contrast distributions of a local region to produce highly efficient feature representation. We extensively evaluated the effectiveness of the new approach with various datasets acquired under varying circumstances. Our experiments demonstrate that our two-component descriptor can represent local region with more information and perform better than SIFT.