The creation of high-tech smart industries is observed in dynamically developing industries, which include the production of electronics and the automotive industry. The concept of “smart manufacturing” is closely related to the concept of cyber-physical systems, which integrates the main elements of digitalization and intellectualization. This concept provides for the continuous improvement of intellectual “cybernetic” resources for the effective management of the “physical” environment considered in this problem area. Improvement of technologies, ensuring high rates of reproducibility and suitability of equipment creates conditions for defect-free production. However, there remain the problems of recognizing patterns represented not by an obvious marriage, but by some not fully defined inconsistency on a set of requirements. The need to disclose uncertainties of this kind is typical for surface mounting technologies for printed circuit boards. The introduction of more and more advanced automatic optical inspections, containing the possibility of introducing intelligent (cybernetic) means, creates conditions for improving the quality of printed circuit boards as a “physical” environment. It is also important to minimize the “human factor”, the presence of which is still used when making decisions on the results of control. In the article, ensuring the rhythm of digital production and increasing the reliability of control in quality management in smart high-tech industries using the example of electronics production.
The paper proposes the implementation of the method of optical recognition of technical documentation and the transformation of graphic information into a machine-readable form available for cognitive analysis, which is based on the methods of binarization and alignment of images, text segmentation and recognition. The use of the proposed method will provide a dramatic reduction in the costs of cataloging, checking the completeness and inventory of documentation, as well as an increase in design quality due to the semantic analysis of documentation using a knowledge base that is updated automatically. The article presents the development of the algorithm for optical recognition of a document, preparation of an image for optical recognition of a document, an example of the application of the Sauvola method for binarization of an image, and an analysis of the research results. The proposed implementation allows the text recognition on scanned/photographed documents.
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