Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. This research work predicts gender from handwriting using the landmarks of differences between the two genders. The relation between gender and handwriting can be seen from the physical appearance of the handwriting.
Out of all the applications gender prediction is mainly admired topic among researchers. Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. 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. 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. 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. 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. 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. It is a term used for the body measurements and calculations. 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 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. 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 the process of determining the author or writer of the text by matching it with the training database. Writer identification is a challenging move in the field of pattern recognition and reflects advanced perceptions into the handwriting research. Results comparable to other methods in the literature are obtained from the proposed method.
#Telugu sahityam pdf file Offline#
Offline handwritten documents written in two different Indic languages, viz.,Telugu and Kannada, are considered for the experimentation. Support Vector Machine is used for the classification purpose. For a given document, based on frequency of occurrence of elements in the stroke alphabet, a histogram is created which represents the writer’s writing style. The paper proposes a clustering method with a new clustering score whereby an optimum number of clusters (categories) are automatically identified. A data driven approach for stroke alphabet creation is done as follows: strokes are extracted from the image, using a regression method, extracted strokes are represented as fixed length vectors in a vector space, strokes are clustered in to stroke categories to create a stroke alphabet. This paper proposes to represent an offline handwritten document with a distribution of strokes over an alphabet of strokes for writer identification.
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