Word retrieval in document images without OCR

We describe a method for efficient indexing and retrieval of words in collections of document images. The approach is based on two main principles: unsupervised prototype clustering, and string encoding for efficient string matching. During indexing, a self organizing map (SOM) is trained so as to cluster together similar symbols (character-like objects) in a sub-set of the documents to be stored. Word images in the collection are grouped accordingto their aspect-ratio. The wordsin each group are represented with . a fìxed-Iength description by using the trained SOM. These representations can be easily compared to score most similar words in response to a user query. The system can be automatically adapted to different languages and font styles. The most appropriate applications are for the processing of old documents (18th and 19th Centuries) where current OCRs have more difficulties. Experimental results describe three application scenarios having various levels of difficulty for current OCR systems.