Showing papers on "Optical character recognition published in 1986"

Document Image Binarization: Evaluation Of Algorithms

Abstract: The extraction of a binary image from a gray level image is a common image processing operation particularly for document image analysis and optical character recognition. Various methods for this task are described in the literature including global and adaptive binarization. This paper evaluates three adaptive binarization techniques viz., a contrast measure approach, a weighted running average approach and a second derivative approach, and compares them to global binarization methods. Experiments with noisy document (postal letter mail) images lead to the following conclusions. Image contrast binarization often yields nearly the same results as the edge operator, with considerably less computation and is less sensitive to parameter settings. In addition, the edge operator is more sensitive to image resolution than the contrast operator. The weighted running-average approach is highly sensitive to the parameters involved in the calculation of the average but produces a quick binarization.

Learning Techniques Applied To Multi-Font Character Recognition

Abstract: In this paper we present the usefulness of symbolic learning techniques for multi-font character recognition. In our already existing models of learning, knowledge is provided and the goal is to find a generalization of given examples, (while for our present model of character recognition knowledge has to be found or rather modified in order to discover a discriminating generalization. An inventive refining of knowledge has allowed to achieve the multi-font character recognition.

Automatic Pattern Recognition System With Self-Learning Algorithm Based On Feature Template Matching

Abstract: A new self-learning technique has been developed to increase recognition efficiency and improve operability of an automatic pattern recognition system. The new algorithm can automatically make feature templates that emphasize the difference between similar patterns. This algorithm compares all the templates with each other by cross-correlating and picks out similar pattern pairs. The differences between similar patterns are extracted as the feature templates. This system can automatically carry out this procedure for 100 template patterns in 5 minutes.

Vision Systems for Character Recognition in Industrial Applications

Abstract: The problem. often, arises in production processes of identifying work-pieces by means of an identification number or other alpha-numerical symbols, for the purpose, for instance - of control of a multiple-branched material flow, - of supervision of progress in manufacture, - of individual work-piece-specific storage of quality data in a quality monitoring system. Various systems can be used for this purpose. One can make use of traditional methods, using cards which indicate work orders and further informations. On the other hand, mechanical codes or microwave-based information-units can be affixed to the transportation system (e.g. pallet) for the purpose of automatic identification. Bar codes nowadays have a dominant position in the field of encoding of information in automated material-flow and manufacturing systems.