Hough transform for line recognition complexity of evidence accumulation and cluster detection
Computer Vision, Graphics, and Image Processing
Intelligent forms processing system
Machine Vision and Applications - Special issue: document image analysis techniques
Form Item Extraction Based on Line Searching
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Form Processing based on Background Region Analysis
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Four directional adjacency graphs (FDAG) and their application in locating fields in forms
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Form Identification Based on Cell Structure
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Analyzing Form Images by Using Line-Shared-Adjecent Cell Relations
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Model matching based on association graph for form image understanding
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
A Unified Algorithm for Identification of Various Tabular Structures from Document Images
International Journal of Digital Library Systems
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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%.