Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection." The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.

This paper provides a review of advances in character segmentation. Segmentation methods are listed under four main headings. The operation of attempting to decompose the image into classifiable units on the basis of general image features is called "dissection". The second class of methods avoids dissection, and segments the image either explicitly, by classification of specified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.

A prototype system for encoding chemical structure diagrams from scanned printed documents is described. The system distinguishes a structure diagram from other printed material on a page image using size and spacing characteristics. It distinguishes line graphics from symbols in an intermediate vectorization stage. Line information is mapped into a connection diagram that represents atomic bonds. Atomic symbols are identified by means of chemical drawing conventions and optical character recognition. The final coded output interfaces to conventional chemistry software for database storage and retrieval, publishing, and modeling.>

A sequential optical character recognition algorithm, ideally suited for implementation by means of microprocessors with limited storage capabilities, is formulated in terms of a binary decision tree. Upper bounds On the recognition performance are derived in terms of the stability of the digitized picture elements. The design process is described in detail. The algorithm is tested on single-font typewritten characters and the experimental and theoretical results are compared.

This paper treats a generalised storage management problem in which the pages have varying sizes and the cost of a page fault is a function of the particular page reference. In addition, cost may be influenced by functional dependencies between pages. In such an environment the conventional page replacement algorithms are found to perform inadequately. New replacement algorithms are proposed for managing the content of a buffer storage area in response to a series of such page references. Simulation experiments were conducted to compare the cost performance of these algorithms against standard techniques. An analytical model of the problem yields an upper bound on performance, and offers justification for the intuitively designed replacement algorithms. One practical environment in which the general problem may arise is a relational data base having an implied relations facility, in which some relations are maintained in definition form until queried. The implied relation is analogous to a page, and the processing time for restructuring a relation from its definition varies from one relation to another. A suitable replacement algorithm is needed to manage the process as the implied relations alternate between the state of definitions and the state of explicit representation in a fixed buffer storage area.

Abstract—An unconventional approach to character recognition is developed. The resulting system is based solely on the statistical properties of the language, therefore it can read printed text with no previous training or a priori information about the structure of the characters. The known letter-pair frequencies of the language are used to identify the printed symbols in the following manner.

The problem of recognizing a large alphabet (1000 different characters) is approached using a two stage process. In the first stage of design, the data is partitioned into groups of similar characters by means of heuristic and iterative algorithms. In the second stage, peephole templates are generated for each character in such a way as to guarantee discrimination against other characters in the same similarity class. Recognition is preceded by establishing an order of search through the groups with a relatively small number of ``group masks.'' The character is then identified by means of the ``individual masks.'' through a threshold criterion. The effects on the error and reject rates of varying the several parameters in the design and test procedure are described on the basis of computer simulation experiments on a 20 000 character data set. An error rate of 1 percent with 7 percent rejects, is obtained on new data.