Hongxi Wei

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—This paper presents a new method to recognize machine-printed traditional Mongolian characters by using back-propagation (BP) neural networks. First, the set of traditional Mongolian characters is divided into five subsets according to each character's position (initial, medial or final) within a word and some steady structural features. Then, each subset(More)
In order to recognize and retrieve the Mongolian Kanjur images, lots of preprocessing tasks should be done. In this paper, we concentrate on the binarization of the Mongolian Kanjur images and we have proposed an efficient binarization method for them. The proposed method is applied to each image as follows: First, some preprocessing tasks including(More)
In this paper, we propose a keyword retrieval system for locating words in historical Mongolian document images. Based on the word spotting technology, a collection of historical Mongolian document images is converted into a collection of word images by word segmentation, and a number of profile-based features are extracted to represent word images. For(More)
—There are many classical Mongolian historical documents which are reserved in image form, and as a result it is difficult for us to explore and retrieve them. In this paper, we investigate the peculiarities of classical Mongolian documents and propose an approach to recognize the words in them. We design an algorithm to segment the Mongolian words into(More)
This paper proposes a knowledge-based system to recognize historical Mongolian documents in which the words exhibit remarkable variation and character overlapping. According to the characteristics of Mongolian word formation, the system combines a holistic scheme and a segmentation-based scheme for word recognition. Several types of words and isolated(More)