Neural network methods of text image recognition

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Tymchenko O. V., Havrysh B. M., Durniak B. V. № 1 (81) 72-88 Image Image

The paper considers the methodology of improving the quality of recognition of text images, in particular distorted by noise and with geometric distortions due to poor scanning or photography. It has been shown that the problem of text document recognition is clo­sely related to the problem of pattern recognition, so that methods of comparison with a sample, statistical and based on neural networks can be applied to text recognition. The comparison of methods shows the expediency of using structural recognition methods with the use of neural networks.

Methods and algorithms for constructing a fuzzy text image recognition system are considered, a generalized structural scheme of a text image recognition system and func­tions of the main nodes are presented. Methods of pre-processing of text images in order to improve the image quality, convert the initial color image into images in grayscale and reduce noise using linear (Gaussian filter) and nonlinear median filtering. An algorithm for preliminary fuzzy image processing for their segmentation is presented. Features of binarization of such images by means of a threshold surface that allows reducing trhe influence of unevenness of illumination are shown. A fuzzy processing algorithm has been created to highlight boundaries and character segmentation.

For structural recognition of text images the methods of creation of the corresponding grammar with application of the theory of graphs and methods of the theory of formal languages ​​and grammars are considered. The used methods of horizontal and vertical concatenation of image elements are shown. Recognition consists in finding the best in a certain sense output of an image in a given grammar. Methods of training a neural net­work according to the algorithm of error backpropagation are considered and its testing is carried out. Segmentation was introduced for each line of text, and the methods were configured so that the result of line recognition coincided with the entered segmentation.

The result of noise image recognition is presented. A comparison of the work of the developed neuro-fuzzy system (with a window of 5x5 pixels) with the commercial product ABBYY FineReader 11 Corporate Edition (the image of the text of the page is 702 cha­racters, Gaussian noise - 0.03 from the black level). For practical implementation, the Matlab Simulink software environment with built-in fuzzy logic elements Fuzzy Logic Toolbox was chosen. It is concluded that increasing the number of inner layers of the neural network allows to improve the number of correctly recognized symbols, but for a much longer time. The time and spatial complexity of the developed algorithms are determined by the number of fragments that are viewed in the process of their work.

Keywords: recognition system, fuzzy and noisy image processing, text image re­cog­nition, structural recognition, neural networks.

doi: 10.32403/0554-4866-2021-1-81-72-88


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