Individuality analysis of online Kanji handwriting

Nakamura, Yoshikazu; Kidode, Masatsugu

doi:10.1109/icdar.2005.141

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…Recent studies have made significant progress exploring touchscreen gesture recognition in mobile devices during various tasks such as document reading, Virtual keyboard interactions, keystroke dynamics, web browsing, Complex typing patterns, and unstructured tasks [26]- [28]. In the field of writer identification, a range of techniques has been explored, including Support Vector Machines [29], [30], distance-based methods [31], [32], and deep learning techniques [33]. The methods employed for writer identification are diverse and varied.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Hasan,

Rahim,

Shin

et al. 2024

IEEE Access

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Handwriting is controlled by neurons in the brain's nervous system, reflecting an individual's personality and psychology. This unique characteristic can be used for various applications, including user authentication, assessment of neurodegenerative disorders, and classification of handedness, gender, and age groups. Traditional authentication systems require memorization, information leakage, and fingerprints, making them vulnerable to security breaches. The majority of researchers have studied the limitations of image quality, camera frames, and light effects on text and image-dependent performance. Therefore, this paper mainly focused on real-time, text-independent handwriting fine-motor data and proposed an efficient authentication system with low cost using efficient feature extraction and optimal feature selection approaches. This research utilizes two benchmark databases, including the handwriting data of 48 (24+24) participants collected via a sensor-based pen tablet. Each participant wrote the 10 words five times repeatedly, making it a total of 2400 samples. The handwriting classification of the different individuals is in 3 phases: feature extraction, feature selection, and classification. A total of 91 features (statistical, kinematic, spatial, and composite) were extracted from more accurate, real-time numerical handwriting data. The efficient and optimal features have been selected using four feature selection approaches, namely, Pearson's r correlation, ANOVA-F, Mutual Information Gain, and PCA, among which the ANOVA-F test and PCA perform well for handwriting-extracted data. Then, 14 machine learning (ML) models and 7 deep learning (DL) models were applied to handle the problem of individual classification, with both no-and full-feature-selection scenarios considered. The experimental analysis has been conducted with different angles and perspectives, such as K-Fold cross-validation, testing system efficiency considering 5/10/15/24/48 individuals, and in the case of individual tasks. It shows that ML-based algorithms, namely, CATBOOST (99.07%) with ANOVA-F and DL-based models, namely, BiLSTM (98.31%) with PCA-selected features, provide the highest accuracy with dataset 2, among others that advocate the practicality and reliability of choosing this system for user identification.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Hasan,

Rahim,

Shin

et al. 2024

IEEE Access

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“…An online handwritten character database [23] is used to cluster the strokes. This database was recorded with a pen tablet (WACOM, Japan), which includes traces of the XY-coordinates of the pen together with the information on pressures and elevation angles at each of the data points.…”

Section: Selection Of Letters For Learning Calligraphymentioning

confidence: 99%

Learning System for Japanese Kanji Calligraphy with Computerized Supervision

2019

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The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy.

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Online Writer Verification Using Kanji Handwriting

Nakamura

Kidode

2006

Multimedia Content Representation, Classification and Security

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Individuality analysis of online Kanji handwriting

Cited by 9 publications

References 9 publications

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Learning System for Japanese Kanji Calligraphy with Computerized Supervision

Online Writer Verification Using Kanji Handwriting

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