Despite the significant success in the field of text recognition, complex and unsolved problems still exist in this field. In recent years, the recognition accuracy of the English language has greatly increased, while the problem of recognition of hieroglyphs has received much less attention. Hieroglyph recognition or image recognition with Korean, Japanese or Chinese characters have differences from the traditional text recognition task. This article discusses the main differences between hieroglyph languages and the Latin alphabet in the context of image recognition. A light-weight method for recognizing images of the hieroglyphs is proposed and tested on a public dataset of Korean hieroglyph images. Despite the existing solutions, the proposed method is suitable for mobile devices. Its recognition accuracy is better than the accuracy of the open-source OCR framework. The presented method of training embedded net bases on the similarities in the recognition data.
In this paper we propose an original approach to optical video stream recognition system design, introducing new modules for image quality assessment, image quality correction and feedback. The main novelty of the proposed approach lays in combining image quality assessment results with the global dynamic object saliency maps which indicate the importance or the informative value of the corresponding image regions. The approach is applied to the identity documents video stream recognition system, where saliency maps are initially provided by document templates and are dynamically changing over time – for example, according to per-field stopping rules. Experiments demonstrated an increase of such essential recognition systems characteristics as accuracy, reliability and performance.