Title :
Towards a genetic based prototyper for character shapes
Author :
Bontempi, Bruno ; Marcelli, Angelo
Author_Institution :
Dipartimento di Inf. e Sistemistica, Univ. di Napoli Federico II, Italy
Abstract :
The paper describes an attempt to use a machine learning approach to solve the problem of designing the set of prototypes to be used by an OCR system. The learning mechanism, based on a genetic algorithm, is exploited for providing the system with a set of reliable prototypes of the characters able to explain the variability encountered while dealing with specimen produced by different writers. In this framework, a new genetic algorithm with a variable population size is proposed, as well as a shape description scheme devised to improve the efficacy and the efficiency of the genetic search. Preliminary experiments show that the proposed approach is a promising step towards the automatic construction of the set of prototypes to be used for the recognition
Keywords :
genetic algorithms; learning (artificial intelligence); optical character recognition; OCR system; character shapes; genetic algorithm; genetic based prototyper; machine learning; shape description scheme; Algorithm design and analysis; Character recognition; Engines; Genetic algorithms; Handwriting recognition; Learning systems; Prototypes; Robustness; Shape; Writing;
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
DOI :
10.1109/ICDAR.1995.601997
