Machine vs humans in a cursive script reading experiment without linguistic knowledge

This paper presents an overview of a dynamic cursive script recognition approach that uses no linguistic constraints. This approach seeks to recognize in cursive script, morphologically and pragmatically coherent sequences of character hypotheses. As performance is compared with the performance of the best available cursive script recognizers-humans-in a reading experiment where linguistic knowledge is useless. The recognition method uses fuzzy-shape grammars to model the morphological characteristics of conventional letters. These models, called allographs, can be viewed as basic (a priori) knowledge for developing a multi-writer recognition system. Character hypotheses are segmented within a cursive word using a parser for these grammars. Character sequences are then constructed from these segmentation hypotheses using local adjacency constraints also modeled by fuzzy-shape grammars. Two experiments are conducted on a test database containing a handwritten cursive test 600 characters in length written by ten different writers. Results show that the system performances are highly correlated with human performance.