Commercial OCR Systems Research Articles

Methods and algorithms for protecting information in optical text recognition systems

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.

Two template matching approaches to Arabic, Amharic and Latin isolated characters recognition

With the establishment of commercial OCR systems for Latin text, recent research efforts have been directed at the design of recognition systems for non-Latin scripts, such as Japanese, Cyrillic, C...

Binarization of color document images via luminance and saturation color features

This paper presents a novel binarization algorithm for color document images. Conventional thresholding methods do not produce satisfactory binarization results for documents with close or mixed foreground colors and background colors. Initially, statistical image features are extracted from the luminance distribution. Then, a decision-tree based binarization method is proposed, which selects various color features to binarize color document images. First, if the document image colors are concentrated within a limited range, saturation is employed. Second, if the image foreground colors are significant, luminance is adopted. Third, if the image background colors are concentrated within a limited range, luminance is also applied. Fourth, if the total number of pixels with low luminance (less than 60) is limited, saturation is applied; else both luminance and saturation are employed. Our experiments include 519 color images, most of which are uniform invoice and name-card document images. The proposed binarization method generates better results than other available methods in shape and connected-component measurements. Also, the binarization method obtains higher recognition accuracy in a commercial OCR system than other comparable methods.

Binarization and Multithresholding of Document Images Using Connectivity

Thresholding is a common image processing operation applied to gray-scale images to obtain binary or multilevel images. Traditionally, one of two approaches is used: global or locally adaptive processing. However, each of these approaches has a disadvantage: the global approach neglects local information, and the locally adaptive approach neglects global information. A thresholding method is described here that is global in approach, but uses a measure of local information, namely connectivity. Thresholds are found at the intensity levels that best preserve the connectivity of regions within the image. Thus, this method has advantages of both global and locally adaptive approaches. This method is applied here to document images. Experimental comparisons against other thresholding methods show that the connectivity-preserving method yields much improved results. On binary images, this method has been shown to improve subsequent OCR recognition rates from about 95% to 97,5%. More importantly, the new method has been shown to reduce the number of binarization failures (where text is so poorly binarized as to be totally unrecognizable by a commercial OCR system) from 33% to 6% on difficult images. For multilevel document images, as well, the results show similar improvement.