Retrieval And Verification Of Chinese Calligraphy

With rapid development of digitizing technology, the digitization of Chinese historical calligraphy works is marching into a new stage. The original historical calligraphy works are exists in papers, stones, silks or bamboo slips. They are unique, fragile, and can be easily destroyed. The digitizing gives them a secondary medium to keep and to enable universal accessible. But digitizing the historical calligraphy works brings new challenges: As the existing Optical Character Recognition technologies can't convert these calligraphy images into texts, how to retrieve the calligraphy characters and enable the searching services in digital library? As calligraphy works' verification has long been investigated by calligraphists subjectively from the art's field. How to distinguish the genuine calligraphy works from those of forged with objective measures and evidences? This thesis discusses theories, methodologies and technologies of how to retrieve calligraphy works and how to bring objective measures to verify the historical calligraphy works. The main contributions of this thesis are as follows:1. Retrieval of shape-based calligraphy character. Chapter 3 introduces how to extract meaningful features from individual calligraphy character images segmented from the calligraphy image page. Chapter 4 proposes approaches of how to retrieve calligraphy characters without recognition. First shape matrix is construcedt for each calligraphy character based on the contour point's property. Then retrieve the candidate similar to the query in terms of shape. Meanwhile, if the database contains no calligraphy character that meets the user's demands, then a new style of calligraphy character will be generated by learning from two similar styles.2. Fast search engine of calligraphy character. The response time is important for the service of digital library, and so Chapter 5 proposes 3 strateges to speed up the retrieval time. The first is to use a coarse-to-fine process to shorten the total computation time. The second is to employ good algorithms of the Dyanamic projecting histogram warping and the extended 2D Dyanamic Programing Warpping to speed up the shape matching process. The third is to introduce high-dimensional indexer to minimize the access and index time.3. Learning of calligraphy writing style. Calligraphy style is the key to understand different calligraphy and to distingsuish calligraphy works created by differenct calligraphist in different dynasty. Thus Chapter 6 discusses about how to employ machine learning methodologies to represent calligraphy style by suitable features. The features, mainly in the stroke level and in the character level, are selected and the corresponding weights are computed to construct a personal calligraphy style vector.4. Verification of calligraphy works. Chapter 7 presents the system architecture and the basic working principle of calligraphy verification. First, the calligraphyist's genuine mode is proposed according to the name that the suspicious works claimed. Then based on this genuine mode, the features of the suspicious works and the guniue's works are analysized and the accepting probabilities are computed. The final decision is based on the accumulated probabilities and the...