A line-oriented approach to word spotting in handwritten documents

The problem of word spotting in handwritten archives is approached by matching global shape features. A set of visual templates is used to define the keyword class of interest, and initiate a search for words exhibiting high shape similarity to the model sec. Major problems of segmenting cursive script into individual words are avoided by applying line-oriented processing to the document pages. The use of profile oriented features facilitates the application of dynamic programming techniques to pattern matching, and allows us to achieve high levels of recognition performance. Results of experiments with old Spanish manuscripts show a high recognition rate of the proposed approach.