MATRA CAP S ystemes-Departement Lecture Automatique 3,av.duCentre-78052 Saint-Quentin-en-Yvelines -AbstractThe paper describes a recognition scheme for reading handwritten cursive words using three word recognition techniques. It particularly focuses on the implementation used to combine the three techniques based on a comparative sru& of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentatiorr of the word into letters, bur diger in the character ClassiJier they use. The former runs a statistical linear classifier, and the latter runs a neural network with a different representation of the input data. The testing, comparison, and combination studies have been perfornied on word images from mail provided by the USPS. The top choice recognition rates achieved so far correspond to 88 %, 76 %, 6.5 % with respect to lexicon sizes of 10, 100, and IO00 words.
This paper presents a complete numeral amount recognition module which is integrated in an automatic system aimed at reading all types of French checks. This module is combined with an automatic reading system of literal amounts. This complete working system, called LIREChèques, is developed by MATRA MS&I and is now in advanced test at SERINTEL, a pilot site. Two aspects of the numeral amount recognition system are particularly emphasized: the numeral recognition stage itself and the syntactic analysis stage. The numeral recognition module relies on a combination of two individual classifiers, the first one is based on concavity measurements, the second one on both statistical and structural features. The syntactic analysis, called syntactic/contextual analysis, is combined with contextual information to take into account the segmentation behaviour and the presence of literal entities in the numeral amount. We demonstrate that very good performances can be obtained on digits such as those extracted from numeral amounts since a substitution rate of 0.06% while still preserving a recognition rate of near 87% can be achieved. As for the syntactic/contextual analysis stage, results obtained on a test set (containing checks from more than 40 different banks and 15/ of typed checks, thus being a good representation of the real tests realized on site) show clearly that introduction of contextual information in association with syntactic analysis allows to process much more numeral amounts than a simple syntactic analysis and increases perceptibility of the recognition rate.
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