Content-based Information Retrieval from Handwritten Documents

This paper is about retrieving the closest matches from a set of scanned handwritten documents based on a query that is a document image. System indexing and retrieval is based on writer characteristics, textual content as well as document meta data such as writer profile. Documents are indexed using global image features, e.g., stroke width, slant, word gaps, as well local features that describe shapes of characters and words. Image indexing is done automatically using page analysis, page segmentation, line separation, word segmentation and recognition of characters and words. Retrieval is based on a probabilistic model of information retrieval, where feature difference between documents are modeled as probability distributions. The system has been implemented using Microsoft Visual C++ and a relational database system. This paper reports on the performance of the system for retrieving documents based on writing style.