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Graphology

About: Graphology is a research topic. Over the lifetime, 214 publications have been published within this topic receiving 2492 citations.


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Proceedings ArticleDOI
17 Mar 2017
TL;DR: This work implements a system which analyses these features of handwriting and gives a detailed analysis employing the principles of Graphology, and proposed and implemented six different handwriting features.
Abstract: Graphology is an ancient science which uses different attributes of handwriting to analyze the person's personality traits Features like the size of one's handwriting, the slant et al helps in identifying the particular trait associated with the subject Professional handwriting analysts or Graphologists are engaged in hiring recruitments so that a suitable candidate is preferred conforming to the job requirements In this work, we implement a system which analyses these features and gives a detailed analysis employing the principles of Graphology Extraction of six different handwriting features has been proposed and implemented Image Processing was used for feature extraction using MATLAB

16 citations

Book ChapterDOI
01 Jan 2015
TL;DR: Analysis of Devanagari characters for writer identification with 99.12 % accuracy for LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters.
Abstract: This paper presents analysis of Devanagari characters for writer identification. Being originated from Brahmic script, Devanagari is the most popular script in India. It is used by over 400 million people around the world. Application of writer identification of Devanagari handwritten characters covers a vast area such as The Questioned Document Examination (QDE) is an area of the Forensic Science with the main purpose to answer questions related to questioned document (authenticity, authorship and others). Signature verification in banking, in Graphology (study of handwriting) a theory or practice for inferring a person’s character, disposition, and attitudes from their handwriting. Here we collect 5 copies of handwritten characters to nullify intra-writing variation, from 50 different people mainly students. After preprocessing and character extraction, 64-dimensional feature is computed based on gradient of the images. Some manual processing is required because some noises are too difficult to remove automatically as they are much closer to the characters. We have used LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters. We have done the writer identification with all the characters and obtained 99.12 % accuracy for LIBLINEAR with all writers. Features collected from this work can be used in the next level to identify writers from their cursive writing.

15 citations

01 Jan 2013
TL;DR: Using combination of graphical approach based on signature and digit of character of application form using multi-structure algorithms and artificial neural networks, a sketch of the writer's character traits, emotional disposition and social style using standard of graphology is generated.
Abstract: Handwriting stroke reflects the written trace of each individual's rhythm and style. 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 combination of graphical approach based on signature and digit of character of application form using multi-structure algorithms and artificial neural networks (ANN). The image split into two areas: the signature based on nine features and application form of letters digit area. Each area had preprocessing performed to improve the recognition accuracy. Signature area is classified using ANN based on five features which result an accuracy of 5678%. While four feature of the signature that detection using multi structure algorithm result 87-100% accuracy. In meantime, pattern recognition of application form digit area using Learning Vector Quantization gave 43% accuracy. It used 100 sets of data testing after training with 10-25 data. The system has been implemented with the software so that it can be used for classification of personality from handwriting scanned automatically.

15 citations

Journal ArticleDOI
18 Feb 2018
TL;DR: This study was conducted by taking 42 samples of handwriting from different backgrounds and showed the accurate average of the application reached 82.738%.
Abstract: Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of one’s personality through graphology. In addition, by using android based mobile device, graphology analysis could show one’s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.

15 citations

Journal ArticleDOI
TL;DR: In this paper, an evaluation of the current published scientific reviews on the use of graphology in personnel selection, and several additional studies graphologists provided that seemed to have been overlooked are presented.
Abstract: Among the various tests employed in personnel selection, handwriting analysis, or graphology, has enjoyed long-standing international popularity despite being highly contentious. This report contains not only an evaluation of the current published scientific reviews on the use of graphology in personnel selection, but also an evaluation of several additional studies graphologists provided that seemed to have been overlooked. The latter were obtained by contacting nine of the foremost institutes offering graphological training, consulting services, or both to ensure that the graphologists themselves would be fairly represented. Even with this additional information we found no reason to counter conclusions the scientific community has reached, namely that (a) the continued use of graphology in personnel selection could prove harmful to many individuals and firms, and (b) it fails to approach the level of criterion validity of other widely available and less expensive screening devices used for personnel se...

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20218
20208
201915
201812
201712
20168