A method to determine a distance measure between two nonhierarchical attributed relational graphs is presented. In order to apply this distance measure, the graphs are characterised by descriptive graph grammars (DGG). The proposed distance measure is based on the computation of the minimum number of modifications required to transform an input graph into the reference one. Specifically, the distance measure is defined as the cost of recognition of nodes plus the number of transformations which include node insertion, node deletion, branch insertion, branch deletion, node label substitution and branch label substitution. The major difference between the proposed distance measure and the other ones is the consideration of the cost of recognition of nodes in the distance computation. In order to do this, the principal features of the nodes are described by one or several cost functions which are used to compute the similarity between the input nodes and the reference ones. Finally, an application of this distance measure to the recognition of lower case handwritten English characters is presented.

A distance measure between attributed relational graphs for pattern recognition

Research and development of OCR systems are considered from a historical point of view. The historical development of commercial systems is included. Both template matching and structure analysis approaches to R&D are considered. It is noted that the two approaches are coming closer and tending to merge. Commercial products are divided into three generations, for each of which some representative OCR systems are chosen and described in some detail. Some comments are made on recent techniques applied to OCR, such as expert systems and neural networks, and some open problems are indicated. The authors' views and hopes regarding future trends are presented. >

Historical review of OCR research and development

: The field of picture processing by computer is reviewed from a technique-oriented standpoint. Only the processing of given pictures (as opposed to computer-synthesized pictures) is considered. Specific areas covered include: (a) Pictures as information sources and their efficient encoding; (b) Approximation of pictures - sampling and quantization techniques; (c) Position-invariant operations on pictures and their implementation (digital, electro-optical, optical); applications to matched filtering (template matching), spatial frequency filtering and image restoration, measurement of image quality, and image enhancement ('smoothing' and 'sharpening'); (d) Picture properties (linear; local and 'textural'; random) useful for pictorial pattern recognition; (e) 'Figure extraction' from pictures; figure properties (topology, size, shape); (f) Picture description and 'picture languages.' (Author)

Picture processing by computer

Based on a study of the extensive literature in handprint recognition, this paper presents a survey in this challenging field. Recognition algorithms, data bases, character models, and handprint standards are examined. Achievements in the recognition of handprinted numerals, alphanumerics, Fortran, and Katakana characters are analyzed and compared. Data quality and constraints, as well as human and machine factors are also described. Characteristics, problems, and actual results on on-line recognition of handprinted characters for different applications are discussed. New emphases and directions are suggested.

Automatic recognition of handprinted characters&#8212;The state of the art

If a picture contains elongated objects of different thicknesses, one can make measurements on it which are thickness-invariant by first transforming it so that each object is thinned down to a \"medial line\" of constant thickness. Several algorithms are described which perform such a thinning transformation when applied to the picture in parallel. It is proved that these algorithms do not change the connectivity properties of the picture.

Some Parallel Thinning Algorithms for Digital Pictures

A prototype of a postal code number reader capable of recognizing handwritten arabic numerals has been developed and tested successfully. Recognition of handwritten arabic numerals requires sufficient system flexibility to cope with both unlimited variations in character shape and a large number of writing instruments. This flexibility has been achieved by combining the use of a stored recognition table and a hardware microprogram. The recognition logic obtained by computer simulation is immediately stored in the core memory as a table. The numerals are recognized by extracting a sequence of the geometrical features in horizontal zones of the character after normalization of the height of the character and the width of the strokes. The use of sequential recognition logic helps reduce the capacity of core memory required to obtain the desired high rate of recognition. The correct recognition rate of a single digit for a large sample of letters averaged 95 percent. For a three-digit sorting with 13 stackers the sorter incorrectly recognized approximately 0.1 percent, a rate of error consistent with manual sorting.

http://ieeexplore.ieee.org/iel5/5/31111/01448501.pdf

S. Katsuragi

Papers

Recognition of handwritten numerical characters for automatic letter sorting