Improving semi-text-independent method of writer verification using difference vector

Xin Li; Xiaoqing Ding

doi:10.1117/12.805235

19 January 2009 Improving semi-text-independent method of writer verification using difference vector

Xin Li, Xiaoqing Ding

Proceedings Volume 7247, Document Recognition and Retrieval XVI; 72470Q (2009) https://doi.org/10.1117/12.805235
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

The semi-text-independent method of writer verification based on the linear framework is a method that can use all characters of two handwritings to discriminate the writers in the condition of knowing the text contents. The handwritings are allowed to just have small numbers of even totally different characters. This fills the vacancy of the classical text-dependent methods and the text-independent methods of writer verification. Moreover, the information, what every character is, is used for the semi-text-independent method in this paper. Two types of standard templates, generated from many writer-unknown handwritten samples and printed samples of each character, are introduced to represent the content information of each character. The difference vectors of the character samples are gotten by subtracting the standard templates from the original feature vectors and used to replace the original vectors in the process of writer verification. By removing a large amount of content information and remaining the style information, the verification accuracy of the semi-text-independent method is improved. On a handwriting database involving 30 writers, when the query handwriting and the reference handwriting are composed of 30 distinct characters respectively, the average equal error rate (EER) of writer verification reaches 9.96%. And when the handwritings contain 50 characters, the average EER falls to 6.34%, which is 23.9% lower than the EER of not using the difference vectors.

Citation Download Citation

Xin Li and Xiaoqing Ding "Improving semi-text-independent method of writer verification using difference vector", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470Q (19 January 2009); https://doi.org/10.1117/12.805235

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KEYWORDS

Databases

Principal component analysis

Feature extraction

Optical character recognition

Lithium

Statistical analysis

Image segmentation

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