Mathematical model and a parallel algorithm for processing images containing symbolic information about products

Аuthors

Khomyakov O. O.^*, Panishchev V. S.^**, Titov V. S.^***, Vatutin E. I.^****

South-Western State University, 94, 50-let Oktyabrya str., Kursk, 305040, Russia

*e-mail: homyakov46rus@yandex.ru
**e-mail: gskunk@yandex.ru
***e-mail: titov-kstu@rambler.ru
****e-mail: evatutin@rambler.ru

Abstract

The purpose of the study consists in developing a mathematical model and algorithm for parallel processing of symbolic information on the product labels to increase the character recognition accuracy in the presence of noise and distortion.
The authors employed image processing methods for image regions highlighting, distortions elimination and noise compensation. A neural network approach was applied for the text search and recognition. Methods of parallel computing systems designing were employed as well, and computer modeling methods were applied for testing the developed solution. The authors analyzed methods employed for video stream frames processing to highlight certain objects, and methods used for digital image processing, particularly for the text information extraction. An algorithm for the parts of text information classification based on reference features has been developed. These methods and algorithms have been tested under various conditions. The character classifier implementation was compared with an alternative version. Examples and results of the developed system are demonstrably presented. The proposed solution can be successfully integrated into production process automation systems. It is suitable for a wide range of applications, including product quality control, inventory and warehouse operations automation. The study demonstrates that compared to the analogues, the developed model and algorithm are of higher accuracy and throughput at video streams processing and symbolic information recognition. Compared to the analog, the accuracy increase was from 17% to 30%, depending on the noise level in the image, and the bandwidth was increased by 50%.

Keywords:

character information recognition, OCR, image preprocessing, video stream processing, text information extraction, classification, image recognition, labeling

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