Fuzzy-Membership Based Writer Identification from Handwritten Devnagari Script

Abstract

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

Automated handwriting analysis based on pattern recognition: a survey

“…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].…”

“…In research [30], the authors have been proposed fuzzy C-means as a classifier and the psychological method that used a series of questions to determine a human personality called enneagram and it archived the accuracy with 81.6%. In research [32], a fuzzy membership classifier is has been used to identify writer identification from handwriting Devanagari script and it gave accuracy with 97% on the test set. Another research has been used the fuzzy Sugeno model and proposed a promising framework in handwriting analysis [36].…”

“…It should be noted that there is a lack of research using databases of non-western scripts. Besides that, this would be of great interest since scripts have many symbolic elements that could produce useful information [32,33]. Taking handwriting samples from participants should be repeated much time to study human psychological change and its effects on writing (several times a month or even a year to determine personality shifts and hence conclude in mental disorders that affect the subject or psychological symptoms of a physical disease) [4].…”

Writer identification approach by holistic graphometric features using off-line handwritten words

“…Therefore, the research of this area is being increased. For off-line system, it can be observed in many related works for different languages, mainly in Latin [3], Arabic [3][4][5], and Chinese [3,6], but also in Korean [7] or Devnagari [8], between others. In the next paragraphs, some of those works will be cited.…”

Novel Approaches for Applying Linguistic Processing Techniques Based on Pattern Recognition and Machine Learning

“…Kumar et al [31] propose a novel approach and development for fuzzy-membership based writer identification for handwritten Devnagari script. This paper provides the defining structural properties from graphologists and handwriting experts, fuzzy subsets that use structural linguistic variables described by handwriting experts, and an estimation of fuzzy membership values as feature values.…”