Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine

Prasad, Shitala; Singh, V. P.; Sapre, Akshay

doi:10.5120/1256-1758

Cited by 52 publications

(26 citation statements)

References 9 publications

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“…size of letters, slant of letters and words, baseline, pen pressure, spacing between letters and spacing between words in a document to identify the personality of the writer. It used SVM that generated various parameters are then calculated by simple use of trigonometry and threshold technique [6]. While other research using the polygon method for pattern recognition based handwriting baseline, slant letter and pen pressure, and the identification of letters i and f using template matching as the input of neural networks [7].…”

Section: Introductionmentioning

confidence: 99%

Application image processing to predict personality based on structure of handwriting and signature

Djamal

Darmawati

Ramdlan

2013

2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

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Handwriting stroke reflects how the author faced his world and the emotional honesty. By examining all elements of handwriting and interpreting them separately or integrated, we could generate a sketch of the writer's character traits, emotional disposition and social style using standard of graphology. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. In this research, using graphical approach based on a combination of signature and handwriting to predict the more personality using structure algorithms and multiple artificial neural networks (ANN). The image in A4 paper split into two areas: Signature area which nine features and handwriting based on five features. Each area had pre-processing performed to improve the recognition accuracy. Signature area is classified using ANN based on five features and using multi structure algorithms based on four features. While the handwriting area is classified using multi structure algorithm based on four features (margins, spacing between words and lines, and zone domination) and using ANN after hill valley extraction based on baseline features. Eight features are processed using multi-structure algorithms that provide 87-100% accuracy. In the meantime, six features are classified using an ANN which result an accuracy of 52-100%. It used 100 sets of data testing after training using back propagation with 25-75 data. The system has been implemented with the software so that it can be used for classification of personality from handwriting scanned automatically.

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Section: Introductionmentioning

confidence: 99%

Application image processing to predict personality based on structure of handwriting and signature

Djamal

Darmawati

Ramdlan

2013

2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

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“…person. Everyday conceptions of personality trait [2] [10] make two key assumptions. First are stable over time.…”

Section: Main Textmentioning

confidence: 99%

Personality Trait Identification Using Unconstrained Cursive and Mood Invariant Handwritten Text

Asra¹,

D.C²

2015

IJEME

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Identification of Personality is a complex process. Personality traits are stable over time .Individual"s behavior naturally varies from occasion to occasion. But there is a core consistency which defines the true nature. The paper addresses this issue of behavior. Graphology is normally a technique used to identify the traits. Accuracy of this technique depends on how skilled the analyst is. Although human intervention in handwriting analysis has been effective, but it is costly and prone to fatigue. An automation of handwritten text is proposed. Basically we have considered three important features in the direction of orientation of the lines :(i) up hill (ii) down hill (iii) constant line. Edge histogram and bounding boxes was used for feature extraction .Known classifiers like SVM & ANN are used for training and the results were compared. The results were about 98% for SVM & 70% with ANN. The analysis was done using single line.

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“…size of letters, slant of letters and words, baseline, pen pressure, spacing between letters and spacing between words in a document to identify the personality of the writer. It used SVM that generated various parameters are then calculated by simple use of trigonometry and threshold technique [3]. While other research using the polygon method for pattern recognition based handwriting baseline, letter slant and pen pressure, and the identification of letters i and f using template matching as the input of neural networks [4], and toward the size of 'a' letters using backpropagation algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

Identification of Speed and Unique Letter of Handwriting Using Wavelet and Neural Networks

Djamal¹

2015

EECSI

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Graphology is scientific method to evaluation personality and emotion condition through handwriting and signature. There are many features to identify personality so that previous researches made handwriting analysis automatically. There are page margins, spacing, baseline, vertical zone, font size, slant, pen pressure, and the type t letter. While other studies used features of signature. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. This research integrated both of approach to identify personality of handwriting. It used speed and the type of a, d, i m, t letters as features using structure analysis and artificial neural networks. Type of letter recognition was done after character segmentation. Wavelet transform was used to improve recognition. The proposed methods could be used to identify personality of handwriting. Identification of speed feature using structure analysis toward page margin, spacing between lines, and spacing between words that gave 81% accuracy. While identification of unique letters using neural network with multilayer perceptron architecture, which gave 74% accuracy. Variations training data greatly affect recognition.

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Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine

Cited by 52 publications

References 9 publications

Application image processing to predict personality based on structure of handwriting and signature

Application image processing to predict personality based on structure of handwriting and signature

Personality Trait Identification Using Unconstrained Cursive and Mood Invariant Handwritten Text

Identification of Speed and Unique Letter of Handwriting Using Wavelet and Neural Networks

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