Human identification of letters in mixed-script handwriting: an upper bound on recognition rates

This paper focuses on a reading task consisting of the identification of letters in mixed-script handwritten words. This task is performed by humans using extended or limited linguistic context. Their performance rate is to give an upper bound on recognition rates of computer programs designed to recognize handwritten letters in mixed-script writing. Many recognition algorithms are being developed in the research community, and there is a need for establishing ways to compare them. As some effort is on its way to give large test sets with standard formats, we propose an algorithm to determine a test set of reduced size that is appropriate for the task to achieve (the type of texts or words to be recognized). Also, with respect to a particular task, we propose a method for finding an upper limit to the letter recognition rate to aim for.