Stroke Segmentation Research Articles

False stroke detection and elimination for character recognition

Abstract Thinning is a process by which a binary pattern is transformed into another binary pattern consisting of its skeleton in order to reduce and extract information for further transmission or recognition. It is very important to preserve the shape of the original unthinned pattern in order to keep the necessary information for further processing. However, since thinning algorithms can only consider local information, a skeletonizing transformation generally results in distorted representation of the original unthinned characters, and usually affects the possibility of correct recognition. In the paper, a new technique called ‘dynamical threshold’ is designed to extract the correct features of original patterns. Hence, a more accurate stroke segmentation, having the same effectiveness as human vision, can be achieved to tell how the lines (strokes) overlap (pass through) each other. It provides a method to segment complex stroke structures into strokes from which the structures are composed.

Off-line Arabic character recognition

A personal computer-based Arabic character recognition system that performs three preprocessing stages sequentially, thinning, stroke segmentation, and sampling, is described. The eight-direction code used for stroke representation and classification, the character classification done at primary and secondary levels, and the contextual postprocessor used for error detection and correction are described. Experimental results obtained using samples of handwritten and typewritten Arabic words are presented. >

RECOGNIZING HANDWRITTEN CHINESE CHARACTERS BY STROKE-SEGMENT MATCHING USING AN ITERATION SCHEME

The problem of handwritten Chinese character recognition is solved by matching character stroke segments using an iteration scheme. Length and orientation similarity properties, and coordinate overlapping ratios are used to define a measure of similarity between any two stroke segments. The initial measures of similarity between the stroke segments of the input and template characters are used to set up a match network which includes all the match relationships between the input and template stroke segments. Based on the concept of at-most-one to one mapping an iteration scheme is employed to adjust the match relationships, using the contextual information implicitly contained in the match network, so that the match relationships can get into a stable state. From the final match relationships, matched stroke-segment pairs are determined by a mutually-best match strategy and the degree of similarity between the input and each template character is evaluated accordingly. Certain structure information of Chinese characters is also used in the evaluation process. The experimental results show that the proposed approach is effective. For recognition of Chinese characters written by a specific person, the recognition rate is about 96%. If the characters of the first three ranks are checked in counting the recognition rate, the rate rises to 99.6%.

Stroke segmentation by bernstein-bezier curve fitting

Stroke segmentation is essential to the recognition of handwritten Chinese characters. Here some new and reliable techniques are proposed. First, the thinning process is applied to each character to obtain the skeleton, then a maximum circle technique is used to remove the spurious branches and merge the split 4- fork points. All possible pairs of stroke segments connected at the same fork point are considered, and the Bernstein-Bezier curve is used to fit each pair to smooth the data and find its trend. Then from the result of this curve fitting we can decide which pair belongs to the same stroke. Finally the inflection points with very small radius of curvature are found, and the stroke segmentation is carried out based on these inflection points. Experiments on some Chinese characters show that the proposed new techniques are reliable and time saving compared with the direction method.

Optical character recognition based on analog preprocessing and automatic feature extraction

This paper presents a flexible character recognition method with the emphasis on preprocessing and feature extraction. Spatial band-pass filtering and a white-black decision rule, are applied to extract the different stroke segments of characters. The optical system as a low-pass isotropic filter fulfills the basic operation of smoothing. The analog electrical system operates with the video signals on the output of the integrated photosensing array. A structural-statistical algorithm is used for character recognition. Feature set is generated automatically by means of character segmentation into structural parts and clustering. Filter modelling computer simulation results and simulation results of character recognition are presented. Character recognition results when a feature system are selected by man and when generated by machine are compared.

Constrained bidirectional propogation and stroke segmentation

A new method for decomposing a complex figure into its constituent strokes is described. This method, based on constrained bidirectional propogation, is suitable for parallel processing.Examples of its application to the segmentation of Chinese characters are presented.

Thinning and stroke segmentation for handwritten Chinese character recognition

Firstly, a thinning technique by means of stroke tracking is proposed. The method is considered to preserve the straightness of strokes and the length, which is useful for the stroke segmentation procedure on the recognition of handwritten Chinese characters. Secondly, a method for stroke segmentation, i.c. a way of breaking down a character to a set of consecutive partial strokes, is proposed, which works well owing to the favourable properties of the proposed thinning technique. The method consists of five procedures: extraction of feature pixels, calculation of stroke directions, piecewise linear representation of strokes, unification of intersections and extraction of the consecutive partial strokes.

Machine recognition of handwritten words: A project report

A project beginning with publication of the author's book Recognition in 1965 has resulted in a cursive script recognition program which has correctly identified 79 per cent of a test sample of 84 words. This program makes no use of information about sequence of stroke inscription, and segments the cursive line as part of the recognition procedure. It thus compares favorably in performance level with previously reported programs appropriately “normalized”, while not requiring input pertaining to stroke sequence and stroke segmentation that is essential to these other programs.