The design and performance of a content-based information retrieval system for handwritten documents is described. 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

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... n, word segmentation and recognition of characters and words. Several types of queries are permitted: (i) entire document image; (ii) a region of interest (ROI) of a document; (iii) a word image; and (iv) textual. Retrieval is based on a probabilistic model of information retrieval. 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 same and different content.