On the Use of Lexeme Features for Writer Verification

Document examiners use a variety of features to analyze a given handwritten document for writer verification. The challenge in the automatic classification of a pair of documents to belong to the same or different writer, are both (i)The task of proper selection and extraction of features from the handwritten document and (ii)The use of a proper model that is capable of utilizing the true discriminatory power of these features for classification. This paper describes the use of content specific skeleton based features for characters and pairs of characters (bigrams) and ascertains their discriminatory power. A triangulation skeletonisation procedure is first used to obtain the skeleton of the character(s), and features are computed from the skeleton. Experiments and results are conducted on content specific features extracted for two most frequently occurring bigrams (th, he), and characters (d and f). A neural network based on a Bayesian formulation was used to ascertain the discriminability power of these features. To combine these features with previously existing writer verification features, an alternative Naive Bayes model is also described and evaluated. From the results obtained, we conclude that bigram th has the highest discriminatory power followed by character d, f and bigram he. Also the paper highlights the significant increase in performance of writer verification(^ 15% more) with the use of Bayesian neural networks as against the Naive Bayes model.