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Writer identification using redundant writing patterns and dual-factor analysis of variance

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Abstract

Writer identification (WI) is a typical pattern recognition problem with the goal of recognizing the writer of a text from images of his or her handwriting. For handwriting-based applications, a new approach is required that can confirm the writer based on a very small amount of available text. However, due to the limited number of characters and the diverse contents of the available samples, it is still challenging to achieve a high recognition rate on large-scale databases. To solve this problem, this paper proposes an efficient method for offline text-independent WI using redundant writing patterns and dual-factor analysis of variance (DF-ANOVA). In this method, a search for writing patterns in a limited number of handwritten texts is first conducted to generate meaningful codebooks. Then, these writing patterns are described by means of the simplified Wigner distribution function (WDF) and improved directional index histogram (DIH) descriptors to extract both structural and texture features, and the two corresponding feature distances are integrated by means of a fuzzy integral rule to make the final decision. The Fisher discriminant ratio (FDR) is used for feature selection, and DF-ANOVA is used to eliminate the influence of factors associated with the labels of samples on the feature distance. The results of evaluations on the IAM (96.92%), Firemaker (96.4%), and Uyghur2016 (100%) databases illustrate that this system shows strong robustness to an increasing number of writers and a decreasing number of words in the sample texts.

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Acknowledgements

This work was supported by:(1) The Open Project Program Foundation of the Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences (Grant No.: OEIP-O-202009). (2) Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2020D01A76). (3) University Research Program Natural Science Youth Project of Xinjiang Uyghur Autonomous Region , (Grant No.: XJUDU2019Y032).(4) Subject of Bidding for Key Laboratory of Xinjiang Normal University (Grant No: XJNUSYS092018A02).

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Ayixiamu Litifu, Yuchen Yan, Jinsheng Xiao & Hao Jiang

  2. School of Physics and Electronic Information, Xinjiang Normal University, Urumqi, China

    Ayixiamu Litifu

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  1. Ayixiamu Litifu
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  2. Yuchen Yan
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Correspondence to Yuchen Yan or Jinsheng Xiao.

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Litifu, A., Yan, Y., Xiao, J. et al. Writer identification using redundant writing patterns and dual-factor analysis of variance. Appl Intell 51, 8865–8880 (2021). https://doi.org/10.1007/s10489-021-02307-4

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