Legal amount recognition based on the segmentation hypotheses for bank check processing

A sophisticated methodology of legal amount recognition based on the word segmentation hypotheses is introduced for automatic bank check processing. Word segmentation hypotheses are derived according to the grapheme level segmentation results of the legal amount. Novel hybrid schemes of HMM-MLP classifiers are also introduced for producing the ordered legal word recognition results with reliable decision values. These values can be used for obtaining an optimal word segmentation path of over-segmentation hypotheses as well as an efficient rejection criterion of word recognition result. Simulation was performed with CENPARMI bank check database and shows quite encouraging results.