In this paper we describe an off-line handwritten word recognition system applied to the identification of literal french check amounts. It consists of three successive levels denoted as character, word and phrase level, each of them being related to the previous ones via conditional probability distributions. Training is done on character samples extracted from amount images which are modeled as trajectories in some feature space. At word level, guided by a dictionary, an internal character segmentation algorithm is used in order to maximize a global word probability measure. A stochastic grammar for a priori grammar generation probability of a phrase is proposed at the last level. Results obtained on a 1779 amounts data base provided by the SRTP are encouraging, showing our system open to further improvements.
Stochastic trajectory modeling for recognition of unconstrained handwritten words
Proceedings of 3rd International Conference on Document Analysis and Recognition ; 1 ; 508-511 vol.1
01.01.1995
379562 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Stochastic Trajectory Modeling for Recognition of Unconstrained Handwritten Words
British Library Conference Proceedings | 1995
|Recognition of handwritten words using stochastic models
IEEE | 1995
|Recognition of Handwritten Words Using Stochastic Models
British Library Conference Proceedings | 1995
|Unconstrained Handwritten Character Recognition using Metaclasses of Characters
British Library Conference Proceedings | 2005
|