HATCAS: A Handwritten Textual Content Analysis System

Due to the rapid increase in digital textual documents, it’s becoming difficult these days to get exact information from the heavy stream of textual data available over the web or at personal storage of mobile or computers. This resulted in development of text content analysis models to present the required information to the users quickly as per their queries. In this regard automatic content analysis on handwritten text added more challenges. Finding appropriate text contents is quite difficult when it comes to searching it from a handwritten note. In this paper, we present a Handwritten Textual Content Analysis System (HATCAS) which takes up user queries on specific topics and provides concise answers to them from available handwritten notes. The system uses handwritten notes covering different subjects to generate the answers of the user queries. The presented model is tested on varied user queries and the obtained results of the experiments show the efficiency of the model in presenting the answers with an average accuracy of 96%.