One of the most important problem in constructing computer vision systems for embedded and mobile devices is offline recognition of text strings. In this paper, we analyze the problem of text strings recognition process in a video stream using best frame selection. This method allows to incorporate the information from multiple views of the same target object, thus increasing the overall extraction accuracy. A stopping method is proposed, which allows to make an automatic stopping decision, i.e. to terminate the process at the optimal time in order to maximize the responsiveness of the system. Experimental evaluation on open identity document datasets MIDV-500 and MIDV-2019 show that the proposed stopping rule allows to decrease mean error level of the text recognition results in comparison with a baseline approach which stops after a fixed amount of processed frames.
Two dimensional bar codes are widely used in our life: retail shopping, e-ticketing, advertisement. One of the most widespread symbology (type) is Aztec Code. To read a message from Aztec Code, it first of all must be localized in the input image. To simplify this task, the bar code specification introduces a special part called Core Symbol. In current work, a topological localization method for this part is presented. It relies on a connected components extraction, contour signature analysis and uses lines estimated by the fast Hough transform. It is invariant to scaling and rotation transformations of bar code images and is able to deal with partially corrupted Core Symbols. The technique for method quality measurement is provided. As a base line we consider an algorithm with quality equal to 94.61% which follows the ISO recommendations. The obtained result for the proposed algorithm is 99.03%.
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