Methods and algorithms for protecting information in optical text recognition systems - Dergachov, Konstantin; Krasnov, Leonid; Bilozerskyi, Vladislav; Zymovin, Anatoly (Radìoelektronnì ì komp'ûternì sistemi (Online), 2022)
The subject of the study. A concept of OCR systems performance improvement is proposed, which is achieved through the integrated use of special algorithms for preliminary processing of documents picture, an extended set of service functions, and advanced techniques for information protection. Study objectives: development of algorithms that compensate for the influence of the unfavorable points like imperfect lighting conditions overshooting, images geometric deformation, noises, etc., which corrupt the pictured text, on the efficiency of that text recognition. It is needed to provide for a series of service procedures that would ensure adequate data handling while viewing, converting, and storing in standard formats the results, and ensuring the possibility to exchange data in open communication networks. Additionally, it is necessary to ensure the information protection against unauthorized use at the stage of data processing and provide secretiveness of their transmission through the communication channels. Investigation methods and research results: developed and tested algorithms for preliminary picture data processing, namely, for the captured image geometry transformation, picture noise correction with different filters, image binarization when using the adaptive thresholds reduced the negative influence of irregular image portions illumination; in the software, the special services ensure the data processing ease and information protection are affected. In particular, the interactive procedure for text segmentation is proposed, which implies the possibility of anonymizing its fragments and contributes to collecting confidentiality for documents treated. The package for processing document shots contains the face detection algorithm bringing the identification of such information features; it can be used further in the task of face recognition. After the textual doc is recognized, the received data encryption is provided by generating a QR-code and the steganography methods can deliver the privacy of this information transmission. The algorithms' structures are described in detail and the stability of their work under various conditions is investigated. Focused on the case study, docs' text recognition software was developed with the usage of Tesseract version 4.0 optical character recognition program. The program named "HQ Scanner" is written in Python using the present resources of the OpenCV library. An original technique for evaluating the efficiency of algorithms using the criterion of the maximum probability of correct text recognition is implemented in the software. Conclusions. The study results can constitute the basis for developing advanced specialized software for easy-to-use commercial OCR systems.