Writer Identification of Chinese Handwriting Using Grid Microstructure Feature

Authors:
Xin Li;Xiaoqing Ding
Affiliations:
State Key Laboratory of Intelligent Technology and Systems Department of Electronic Engineering, Tsinghua University, Beijing, China 100084;State Key Laboratory of Intelligent Technology and Systems Department of Electronic Engineering, Tsinghua University, Beijing, China 100084
Venue:
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Year:
2009

Citing 6
Cited 0

Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification Using Directional Element Features and Linear Transform

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Writer Identification Using Edge-Based Directional Features

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Text-Independent Writer Identification and Verification Using Textural and Allographic Features

IEEE Transactions on Pattern Analysis and Machine Intelligence
Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text

International Journal on Document Analysis and Recognition
Writer identification of Chinese handwriting documents using hidden Markov tree model

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a histogram-based feature to Chinese writer identification. It is called grid microstructure feature. The feature is extracted from the edge image of the real handwriting image. The positions of edge pixel pairs are used to describe the characteristics in a local grid around every edge pixel. After global statistic, the probability density distribution of different pixel pairs is regarded as the feature representing the writing style of the handwriting. Then the similarity of two handwritings is measured with the improved weighted visions of some original metric. On the HIT-MW Chinese handwriting database involving 240 writers, the best Top-1 identification accuracy is 95.0% and the Top-20 accuracy reaches 99.6%.