Journal ArticleDOI
Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features
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TLDR
It is shown that a combination of two global features, two local features, and majority voting as a recognizer is efficient for writer identification of offline handwritten Kanji characters.Abstract:
Abstract—This paper presents a study on character features and recognizers used for writer identification of offline handwritten Kanji characters. It is shown that a combination of two global features, two local features, and majority voting as a recognizer is efficient for writer identification. We performed experiments using an offline Kanji character database containing one-hundred Kanji characters, each written by one-hundred writers, and fifty samples of each Kanji character for a given writer. The experimental results show that the identification rate is 7 points higher than the conventional method using a single feature and obtained an identification rate higher than 99% by using three character classes. Writer identification based on scanned images of handwritten characters is a useful biometric modality with applications in forensic and historical document analysis. Research on writer identification that uses online characters is widespread, but offline characters lack form for conveying dynamic information. Nevertheless, research on writer identification using offline characters has proposed many features to acquire useful information. We studied efficient character features and recognizers for writer identification of handwritten offline Kanji characters. Kanji consists of logographic Chinese characters adopted in Japanese writing. The text-dependent writer identification in our research uses a character recognition process before writer identification because, in text-dependent writer identification, a character class is assumed to be already recognized. Therefore, most of the research on text-dependent writer identification of handwritten characters has the following characteristics: employs features developed for character recognition, does not use multiple character features, and uses the local features of a character. In this paper, we propose efficient character features and a recognizer for text-dependent writer identification of a handwritten Kanji character (1).read more
Citations
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Journal ArticleDOI
Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey
Shaveta Dargan,Munish Kumar +1 more
TL;DR: It is observed that work done on the writer identification systems with good accuracy rates in Indic scripts is limited as compared to non-Indic scripts and truly presents a future direction.
Journal ArticleDOI
A Novel Approach to Text Dependent Writer Identification of Kannada Handwriting
B. V. Dhandra,M.B. Vijayalaxmi +1 more
TL;DR: The writer identification results show that feature vectors extracted from longer words, words having more structural variation and combination of features of two or more words have higher impact on writer identification.
Journal ArticleDOI
Writer identification using intra-stroke and inter-stroke information for security enhancements in P2P systems
TL;DR: A novel writer identification method for Chinese characters commonly used in Japan which can be used in peer-to-peer (P2P) systems is proposed and demonstrates that any eight Chinese characters are enough to achieve an identification accuracy over 99.9% when the combination of the two algorithms is applied.
Journal ArticleDOI
Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine
TL;DR: In this paper , a support vector machine (SVM)-based classifier was proposed for writer identification using online handwritten Kanji characters and the experimental results showed that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification.
References
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Journal ArticleDOI
Application of majority voting to pattern recognition: an analysis of its behavior and performance
TL;DR: This paper examines the mode of operation of the majority vote method in order to gain a deeper understanding of how and why it works, so that a more solid basis can be provided for its future applications to different data and/or domains.
Proceedings ArticleDOI
Writer Identification for Offline Handwritten Kanji without using Character Recognition Features
Ayumu Soma,Masayuki Arai +1 more
TL;DR: This paper assumes the following is representative of the writers' style: the start points, the end point, the angle of each stroke composing a Kanji character, and the size and position of the Kanji characters.
Journal ArticleDOI
Writer Identification and Verification Using Autoassociative Neural Networks
TL;DR: This paper proposes a writer identification and verification method using autoassociative neural networks and clarified that the high precision writer recognition will be feasible by using the combined character patterns of the same category or the different category in the calculation of errors.
Journal ArticleDOI
Human Visual and Auditory Information. Text-Independent Writer-Recognition Method using Feature Extraction by Induction Field in Vision.
TL;DR: A text-independent writer-recognition method using feature extraction by the induction field model in vision using the strain of the handwritten character for the standard character to recognize writers is developed.