Writer identification is a challenging move in the field of pattern recognition and reflects advanced perceptions into the handwriting research. It is the process of determining the author or writer of the text by matching it with the training database. It is an exigent task because the writing style of an individual is distinct from other because of unique intrinsic characteristics and is different even if the same writer writes that text with the same pen next time. It is concerned with the writing styles, feelings, perception, behavior and the brain of an individual and it is one of the neoteric applications of biometric identification. Biometric identification is the branch of computer science that deals with identification of an individual from a group using unique identifiers such as fingerprints, retina, handwriting and signatures. It is a term used for the body measurements and calculations. This paper presents a comprehensive and transparent panorama on the work done for the writer identification system on different Indic and non-Indic scripts and a widespread view towards this peculiar research area. The structure of the paper comprises introduction, motivation for the work, background, sources of information, schemes, process, reported works, synthesis analysis, study of features and classifiers for writer identification, and finally the conclusion and future directions. The main focus of this paper is to present in a systematic way, the reported works on writer identification systems on Indic scripts such as Bengali, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Tamil and Telugu and Non-Indic scripts such as Arabic, Chinese, French, Persian, Roman and finally exposes the synthesis analysis based on the findings. This study gives the cognizance and beneficial assistance to the novice researchers in this field by providing in a nut shell the studies of various feature extraction methods and classification techniques required for writer identification on both Indic and non-Indic scripts. It is observed that work done on the writer identification systems with good accuracy rates in Indic scripts is limited as compared to non-Indic scripts and truly presents a future direction.

Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey

This study was designed to detect gender through writer's handwriting.The data set was formed through collecting 80 participants handwriting.133 attributes were tested with J48 and ID3 algorithms.Gender detection through handwriting reached 93.75% success rate. Today, graphology is seen as an experimental field of science that is dedicated to suggest ideas about diseases, profession choices, mood and characteristics of a person by investigating his/her handwriting. Graphology is in cooperation with medicine, psychology, sociology or other disciplines that are based on observation. Graphology is used for staff recruitment in business, diagnoses in medicine, identification of criminals in forensics, choosing a profession in education, guidance and counseling and other practices at every level of social structure. It is quite interesting that the number of the scientific studies on graphology is limited around the world, that there are no specific institutions providing education of graphology and that institutions except for a few international corporations do not benefit from graphology at all.In terms of demographic properties, many statistical and mathematical analyses investigate similar and different variables. Especially, differences regarding gender have become subject to research. Therefore, detecting gender through handwriting can give pace to research in other disciplines. Moreover, the research can be useful in any field where gender detection is needed. This study fulfills two objectives. The first one is to find out whether a writer can identify his/her own handwriting. The second objective is to detect the gender of a writer of a text with the help of graphology and computer sciences. The impact of the study is reflected in the fact that findings can be used in fields where gender detection is needed, and that the detection is done with the help of expert and intelligent systems. At the end of the study, gender detection was performed for the individuals by making use of 133 attributes. Then, a decision tree and lists of rules were created with some algorithms. The purpose was to detect the gender of the person by making a character analysis of the handwriting with the help of decision tree formation methods in data mining. The analysis showed that it is possible to detect the gender of a person with the use of the specified attributes. The study reached a success level of 93.75% with ID3 algorithm.

Gender detection and identifying one's handwriting with handwriting analysis

Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.

Individuality of handwriting

A biometric identification system based on single and multiple modalities has been an evolving concept for solving criminal issues, security and privacy maintenance and for checking the authentication of an individual. The writer identification system is a type of biometric identification in which handwriting of an individual is taken as a biometric identifier. It is a system in which the writer can be identified based on his handwritten text. These systems employ machine learning and pattern recognition algorithms for the generation of a framework. In this paper, the authors have presented a novel system for the writer identification based upon the pre-segmented characters of Devanagari script and also presenting comprehensive state-of-the-art work. The experiment is performed on the corpus consisting of five copies of each character of Devanagari script written by 100 different writers, selected randomly at the public places and consisting of total 24,500 samples of Devanagari characters. Four feature extraction methodologies such as zoning, diagonal, transition and peak extent-based features and classification methods such as k-NN and linear SVM are used with identification accuracy of 91.53% when using zoning, transition and peak extent-based features with a linear SVM classifier.

Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM

A framework for analyzing financial behavior using machine learning classification of personality through handwriting analysis

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.

Writer Identification from Handwritten Devanagari Script

Introduction: Mandible being one of the sturdy bone resists damage and therefore its fragments are mostly available after mass disasters like explosions. After the pelvis, the mandible of the skull has significant sexual dimorphism. Many parameters of the mandible altogether make it suitable to identify sex from a given bone. Aim: To determine sex with the help of parameters of the ramus of human mandible. Materials and Methods: This systematic review included the articles from 2000 to 2020 and took a one-year duration. All studies were categorised under dry bone and radiological study. Only original articles of a cross-sectional study, cohort study and case-control study related to ramus and sex determinations were selected online by using available databases like Medline, Embase, and Google Scholar. All selected articles were then analysed for different types of ramus parameters studied. Studies that considered more than one parameter of ramus were included or otherwise rejected. Research articles that mentioned the efficacy of gender identification of bone by considering parameters of the ramus of the mandible were tabulated. Results: Commonly used ramus parameters by different researchers were maximum ramus breadth/width, minimum ramus breadth/width, ramus flexure and ramus height. In addition, coronoid height and projection height was also used by some. In most of the studies, parameters in males were higher than in females whereas gender predictive value was seen higher i.e, 70-83% when a combination of all ramus parameters was taken. In the case of consideration of a single parameter, gender predictive values ranged from 45-79%, and a higher sex predictive value was seen for males. Conclusion: Only when the fragment of the mandible ramus is available, then it can be utilised for gender identification by using ramus width, ramus height along with other parameters based on availability. Ramus alone is a good tool for sex determination in the mandible.

Kishore Thakur

Papers

Writer Identification from Handwritten Devanagari Script

Mandibular Ramus as a Tool For Sexual Determination: A Systematic Review