Artificial Neural Networks for
Document Analysis and Recognition
This page contains material related to the tutorial: "Artificial Neural Networks
for Document Analysis and Recognition" held on:
The research in the area document processing and neural networks have experienced a renewal of interest in the last fifteen years in which some hundreds of papers on document processing applications using connectionist models have appeared in either scientific journals or conference proceedings. Most of them report different neural network models for the recognition of isolated characters (either printed or handwritten), but there are many other significant approaches to the solution of tasks like character segmentation, line removal, region and document classification, signature verification which are not very well-known. In this tutorial, we presents a survey of most significant tasks of document processing where connectionist-based approaches seem to be adequate. Feature-based representations of either graphic items or whole documents are presented, which are subsequently processed using classic connectionist models like multilayer perceptrons, radial basis functions, and learning vector quantization. The major drawbacks of these massively used models are pointed out with special emphasis on their learning from tabula rasa approach and on the rough static data representation they assume. The role of the prior knowledge in the conception of either appropriate architectures or learning algorithms is discussed with specific reference to important learning tasks in the field of document processing. It is also shown that special structured representations, where data are properly modeled by graphs, can be learned from examples by using connectionist models. They can be successfully used for recognition of graphic items, but also for higher level tasks like document classification and retrieval.
The tutorial is addressed to researchers and graduate students in the field of pattern recognition, and in particular to those working in tha area of document image analysis and recognition.
A general background in pattern recognition and document processing is required, whereas most basic concepts of artificial neural networks will be given in the first part of the tutorial.
Artificial Neural Networks: background
Applications to Document Analysis and
Optical Character Recognition
Artificial Neural Networks: background
Applications to Document Analysis and Recognition
Optical Character Recognition (OCR)
Marco Gori received the Laurea in electronic engineering from UniversitÓ
di Firenze, Italy, in 1984, and the Ph.D. degree in 1990 from UniversitÓ
di Bologna, Italy. During the graduate studies, he also a visiting student
at the School of Computer Science (McGill University, Montreal), where
he was involved in problems of automatic speech recognition using artificial
neural networks. In 1992, he became an associate professor of computer
science at UniversitÓ di Firenze and, in November 1995, he joint
the UniversitÓ di Siena, where he is currently professor of computer
science. His main research interests are currently in the areas of neural
networks and pattern recognition, with special emphasis in document analysis
and recognition. He has organized many scientific events in his area of
expertise, like the international summer school on ``Adapting Processing
of Sequences'' held in Salerno on September 1997 and the International
Joint Conference on Neural Networks (July 24-28, 2000), where he acted
as a program chair.
Dr. Gori serves as an Associate Editor of a number of technical journals, including Pattern Recognition, the IEEE Trans. on Neural Networks, Pattern Analysis and Application, and the International Journal on Pattern Recognition and Artificial Intelligence. He is a fellow of the IEEE and is also the Italian chairman of the IEEE Neural Network Council.
Simone Marinai received the Laurea in Electronic Engineering in 1992,
from the UniversitÓ di Firenze, Italy. He obtained the PhD degree
in computer science in 1996 defending a thesis on the extraction of information
from structured documents.
Currently he is Assistant Professor at UniversitÓ di Firenze, where he teaches some parts of the Artificial Intelligence course for topics related to Artificial Neural Networks and Document Processing applications.
His main research interests are in pattern recognition, neural networks, and document processing applications.
He was the chairman of the workshop ``Document Analysis and Understanding for Document Databases'' (DAUDD) held in Firenze in 1999. He is a member of IAPR.