Showing papers on "Graphology published in 2014"

Handwriting Analysis of Human Behaviour Based on Neural Network

Abstract: Graphology or Handwriting analysis is a scientific method of identifying, evaluating and understanding of anyone personality through the strokes and pattern revealed by handwriting. Handwriting reveals the true personality including emotional outlay, honesty, fears and defenses and etc. Handwriting stroke reflects the written trace of each individual's rhythm and Style. The image split into two areas: the signature based on three features and application form of letters digit area. In this research performance evaluation is done by calculating mean square error using Back Propagation Neural Network (BPNN).Human behaviour is analyzed on the basis of signature by using neural network. Human personality recognition is becoming more and more important in the modern world. It helps human to simplify their jobs and solve more typical problems. Handwriting reasoning or Graphology is a scientific method of identifying, recognizing and perceive personality over the move and patterns notify with the handwriting. In Graphology, handwriting is analyzed through structural graphic elements in order to derive information about the writer personality. There are two approaches in graphology i.e. graphical analysis of the structural type of writing and analysis of the type of symbol or letter. Signature analysis includes the first one approach. This research has been to integrate the two approaches. Type of writing in the form of signatures and letters stroked can describe the personality of the author. Some types each letter is written. Meanwhile the use of signatures is usually used to identify certain personality as with appearance of dots, streaks, shapes or shell, and bottom line. This research is a continuation of previous research that only using handwriting based on five features.To further give overview the attribute, the type of handwriting analysis executed on two areas which the author stroked on a piece of A4 cardboard. The first area is the application form which consist 32 boxes that is written in fundamental letters. Each type of handwritten letters is classified using Learning Vector Quantization (LVQ) to get three dominant of attribute trait. The second area is the type of mark analysis. From signature pattern, five features identified by ANN and four features using multi-structure algorithm. Each space had re-processing performed to improve the recognition accuracy. The combination of letter recognition and signature is intended to provide a more complete overview the personality. Each features recognized in parallel that indicates the distinct personality, in order to obtain a review of certain attributes. The success of the system is determined recognition accuracy against graphology test manually. Our approach to modeling human behavior is to consider the human as a device with a large number of internal psychic states, each with its own particular control behavior and interstate transition probabilities. Perhaps the canonical example of this type of model would be a bank of standard linear controllers (e.g., Kalman lters plus a simple control law), each using different dynamics and measurements, sequenced together with a Markov network of probabilistic transitions. The states of the model can be hierarchically organized to describe both short-term and longer-term behaviors; for instance, in the case of driving an automobile, the longer-term behaviors might be passing, following, and turning, while shorter-term behavior would be maintaining lane position and releasing the brake. Such a model of human behavior could be used to produce improved human-machine systems. If the machine could recognize the human"s behavior or, even better, if it could anticipate the human"s behavior, it could adjust itself to serve the human"s needs better. To accomplish this, the machine would need to be able to determine which of the human"s control states was currently active and to predict transitions between control states. Handwriting analysis is also known as graphology which is a method of identifying the traits related to an individual. It helps in understanding personality traits through the strokes and patterns revealed by handwriting. It can expose marks such as emotional and deep instability, which can further lead an individual to engage in deviant behavior. Handwriting is unique to each one individual, regardless of the word formation of an individual"s handwriting; the shape of the character is will reside the same. This is applicable to all languages. Forgery of another person"s handwriting can easily be identified by proper forensic examining characteristics like pen pressure and oscillation. Theoretically, it is difficult to determine personality especially when it is related to forensics.

Personality Analysis Through Handwriting

Abstract: This work discusses about computer aided Graphology i.e. Personality analysis based on handwritten text, in which instead of a Graphologist, a system will be trained to perform the analysis without much human intervention. A real world dataset of handwritten text samples will be maintained and based on few features like Margins, Baseline, Size, Zones etc. depicted in each of the samples and extracted through Image processing techniques and , an approximate analysis of the writer’s personality trait will be done. These traits will then be mapped to the existing personality theories and finally a personality type, temperament and a detailed report will be given for the handwritten sample, as the result of analysis.

Handwriting analysis: the role of age and education

Abstract: The present research was carried out to investigate possible correlation between handwriting and age and education. The samples consisted of people in Hamedan of 200 participants (100 men and 100 women) who were chosen randomly through cluster sampling. The data were collected using demographic questionnaire and researcher-made Check-list of Persian graphology (Persian handwriting). To analyze data, descriptive statistics was used along with Pearson correlation and multiple regressions. Results from this study indicate a strong correlation between handwriting and age and education, Therefore, it can be concluded that graphology seems to be correlated with age and education and some of these features are predicted by people’s handwriting.

Forensic Document Examination Defined

Abstract: Forensic science is the application of science to the law. Forensic document examination is the application of science and analytical techniques to cases involving questioned documents. Forensic Document Examiner (FDE), Questioned Document Examiner, Document Analyst, Document Examiner, and Handwriting Expert are all titles applied to practitioners. Handwriting Expert is not completely accurate because the work involves more diverse examinations than simply handwriting. Most practitioners today refer to themselves as Forensic Document Examiners (FDE). FDEs work in local, state, and federal laboratories, and also in private practice. Cases submitted to FDEs include: forged checks, threatening letters, disputed signatures on wills, trusts, business documents, mortgage documents, typewritten documents, altered business documents, medical records, and photocopied documents. Forensic handwriting identification is based on the principle that no two people write exactly alike. Forensic document examiners are not graphologists. Graphologists attempt to determine character from the analysis of a person’s handwriting. Forensic document examiners compare questioned writing with known writing for identification or elimination purposes.