Treffer: Form Analysis by Neural Classification of Cells
Title:
Form Analysis by Neural Classification of Cells
Authors:
Contributors:
READ (READ), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), EDF R&D (EDF R&D), EDF – Électricité de France (EDF [E.D.F.]), Y. Nakano, S. W. Lee & Y. Nakano
Source:
Third IAPR Workshop on Document Analysis Systems, Y. Nakano, 1998, Nagano, Japan
Publisher Information:
CCSD; IAPR, 1998.
Publication Year:
1998
Collection:
collection:CNRS
collection:INPL
collection:LABO-LORIA-SET
collection:LORIA2
collection:UNIV-LORRAINE
collection:LORIA
collection:EDF
collection:AM2I-UL
collection:INPL
collection:LABO-LORIA-SET
collection:LORIA2
collection:UNIV-LORRAINE
collection:LORIA
collection:EDF
collection:AM2I-UL
Subject Terms:
form cell, neural approach, cell classification, approches neuronales, classification de cellules, form analysis, analyse de formulaires, ACM: J.: Computer Applications, [INFO.INFO-NE]Computer Science [cs], Neural and Evolutionary Computing [cs.NE], [INFO.INFO-OH]Computer Science [cs], Other [cs.OH], [INFO.INFO-TT]Computer Science [cs], Document and Text Processing
Original Identifier:
HAL:
Document Type:
Konferenz
conferenceObject<br />Conference papers
Language:
English
Access URL:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00098513v1
Database:
HAL
Weitere Informationen
Colloque avec actes et comité de lecture.
Our aim in this paper is to present a methodology for linearly combining multi neural classifier for cell analysis of forms. 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 %.