Date field extraction in handwritten documents

Automatic extraction of date patterns from handwritten document involves difficult challenges due to writing styles of different individuals, touching characters and confusion among identification of alphabets and digits. In this paper, we propose a framework for retrieval of date patterns from handwritten documents. The method first classifies word components of each text line into month and non-month class using word level feature. Next, non-month words are segmented into individual components and classified into one of alphabet, digit or punctuation. Using this information of word and character level components, the date patterns are searched first using voting approach and then we detect the candidate lines for numeric and semi-numeric date using regular expression. Gradient based features and Support Vector Machine (SVM) are used in our work for classification. The experiment is performed on handwritten dataset and we have obtained encouraging results from it.