Word Spotting: Indexing Handwritten Archives

There are many historical manuscripts written in a single hand which it would be useful to index. Examples include the early Presidential papers at the Library of Congress and the collected works of W. B. DuBois at the library of the University of Massachusetts. The standard technique for indexing documents is to scan them in, convert them to machine readable form (ASCII) using Optical Character Recognition (OCR) and then index them using a text retrieval engine. However, OCR does not work well on handwriting. Here, an alternative scheme is proposed for indexing such texts. Each page of the document is segmented into words. The images of the words are then matched against each other to create equivalence classes (each equivalence classes contains multiple instances of the same word). The user then provides ASCII equivalents for say the top 2000 equivalence classes. The current paper deals with the matching aspects of this process. Due to variations in even a single person’s handwriting, it is expected that the matching will be the most difficult step in the whole process. Two different techniques for matching words are discussed. The first method, based on Euclidean distance mapping, matches words assuming that the transformation between the words may be modelled by a translation (shift). The second method, based on an algorithm developed by Scott and Longuet-Higgins, matches words assuming that the transformation between the words may be modelled by an affine transform. Experiments are shown demonstrating the feasibility of the approach for indexing handwriting.