Form Analysis by Neural Classification of Cells

Our aim in this paper is to present a generic approach for linearly combining multi neural classifier for cell analysis of forms. This approach can be applied in a preprocessing step in order to highlight the different kind of information filled in the form and to determine the appropriate treatment. Features used for the classification are relative to the text orientation and to its character morphology. Eight classes are extracted among numeric, alphabetic, vertical, horizontal, capitals, etc. Classifiers are multi-layered perceptrons considering firstly global features and refining the classification at each step by looking for more precise features. The recognition rate of the classifiers for 3. 500 cells issued from 19 forms is about 91%.