Automated handwriting analysis system using principles of graphology and image processing

Sen, Anamika; Shah, Harsh

doi:10.1109/iciiecs.2017.8276061

Cited by 19 publications

(8 citation statements)

References 4 publications

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“…Template matching, the simplest classification technique with the concept of similarity: the same patterns can be grouped into the same class. Some letters like "t" and "i" are analyzed and detect personality traits [3][4][5][6][7][8]. A template matching algorithm is used to measure the correlation between the height of the t-bar on the stem of the letter 't' and the title over 'i' letters to determine a person's personality traits.…”

Section: Automated Handwriting Analysis Based On Pattern Recognition: a Survey (Samsuryadi)mentioning

confidence: 99%

“…Many researchers have worked to make the dataset. Several factors must be considered as follows: defining a group of the respondent that might be included the ratio of male-female [19], group of age [6,7,19], specification of paper size [15,20], type of pen (ballpoint or ink pen) and ink colors [20]. After the data is taken, then the data acquisition process is carried out in digital form using a scanner.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…The size of handwriting is among the most essential factors and it reflects how an individual feels about the adaptability of a person, concentration, and nature. The size of handwriting can help to discover an individual's social aptitude [6,18,[29][30][31][32]. The margin of handwriting could be useful in handwriting analysis and the researchers use it to indicate personality characteristics like adjustment, intelligence, past and future, truthfulness, and fastness [3,18,[33][34][35].…”

Section: Feature Extraction On Handwriting Analysismentioning

confidence: 99%

“…In research [24], the different feature has been used to detect personality traits and it gave the accuracy rate of lines segmentation with 95.65%, word segmentation with 92.56% and respectively 96% offline and words were normalized perfectly with tiny error rate. In research [6], rule base algorithm has been applied to analyze features of handwriting and it gave accuracy with 95% accuracy in identifying the handwriting features and assigning the correct trait according to principles of Graphology. The authors also propose a rule-based algorithm based on image processing to extract handwriting features like size and title over 'i' using MATLAB [21].…”

Section: Classification In Handwriting Analysismentioning

confidence: 99%

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Automated handwriting analysis based on pattern recognition: a survey

Samsuryadi¹,

Kurniawan²,

Mohamad³

2021

IJEECS

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<span>Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. </span>

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Section: Automated Handwriting Analysis Based On Pattern Recognition: a Survey (Samsuryadi)mentioning

confidence: 99%

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Section: Feature Extraction On Handwriting Analysismentioning

confidence: 99%

Section: Classification In Handwriting Analysismentioning

confidence: 99%

See 2 more Smart Citations

Automated handwriting analysis based on pattern recognition: a survey

Samsuryadi¹,

Kurniawan²,

Mohamad³

2021

IJEECS

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“…These methods provide a less expensive way to get degree of handedness in minimal amount of time. In this study, dynamic kinematic features of handwriting data are used along with static features unlike optical character recognition (OCR) [54] and image processing methods where the most important time stamp values are lost, and only static features using image pixels are considered [55]. Our analysis showed that the extracted handwriting features per stroke helped in associating score to the degree of handedness.…”

Section: Introductionmentioning

confidence: 99%

Novel Methods for Handwriting-based Automated Grading of Degree of Handedness

Bala¹,

Joshi²

2022

Preprint

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<p>Hand preference and degree of handedness are two different aspects of human behavior which are often confused to be one. While hand preference is affected by various natural, environmental, and socio-cultural causes, degree of handedness reflects the inherent capability of a human gained by nature which can be modified with training. In this paper, we present two novel methods to quantify the degree of handedness for the first time using handwriting features of dominant and non-dominant hand on three categories of subjects- “Unidextrous”, “Partially-Unidextrous”, and “Ambidextrous”. Methods: Time, static, and dynamic variables of handwriting signal were used as the features for quantification of degree of handedness. Davies Bouldin Score as a statistical method and Neural Network accuracy-based 4-point scale as an automated method for this quantification were presented and compared with the well-known degree of handedness assessment questionnaires from Edinburgh Inventory (EI). Results: Davies Bouldin Score and 4-point scale from Neural Network were found to be in accordance with the EI questionnaires. 4-point scale was preferred over Davies Bouldin score as a robust grading mechanism. Conclusion: The presented methods can be used as an assessment tool for quantifying degree of handedness with multiple applications in i) determining the feasibility of switching hand preference under societal norms ii) neuro-rehabilitation strategies iii) neurological disorder diagnosis iv) study of brain lateralization and activation levels v) forensics vi) sports applications vii) human behavioral assessments.</p>

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