Research On Effective Management And New Service Model For Chinese Calligraphy

Chinese calligraphy has become an important and characteristic resource in digital library. With the growing of the digitized calligraphy works, how to utilize the calligraphy resource effectively in digital library is becoming a challenge.This thesis focuses on calligraphy character feature extraction, computer-aided calligraphy tablet design, calligraphy style modeling and relationships mining, as well as massive data retrieval and processing, and discusses some key technical problems about calligraphy resource management and services in digital library. The main contributions of this thesis are as follows:1. Calligraphic character feature representation. Due to the lack of calligraphic character OCR technology as well as the time-comsuming and laborious manual annotation, it is difficult to implement a text-based character retrieval system. In addition, the evolution of Chinese characters and the deformation of calligraphic characters both bring difficulties for character recognition, so there is a need for a character retrieval method which does not rely on the character recognition. Shape representation is the key problem for the shape-based calligraphic character retrieval. In Chapter 3, shape context and HoG feature are combined to represent the shape of character, where the combining parameter is learned by gradient descent algorithm. Moreover, character style feature is the base of calligraphy tablet design, style modeling and style relationships discovery. At the character level, calligraphy style is represented as the statistical similarity of stokes, so Gabor feature, Contourlet feature and pHoG feature are fused by kernel function to represent the calligraphy character style feature.2. Computer-aided calligraphy tablet design. How to generate style-consistent calligraphy tablet with the calligraphy characters in digital library for users is a meanful work. Users can submit a query to the system, and then the system select the corresponding calligraphy characters from the database according the query to form the candidate tablets, and finally rank these tablets according to the style consistency model. In order to address the inaccuracy of low-level feature and subjectivity of appreciation, the paper introduces the context feature and feedback technologies to adjust the style similarity measurement and to improve the quality of the generated tablets.3. Calligraphy style modeling. Style modeling the key for style based calligraphy works management. The paper introduces a generative probabilistic model for automatically extracting a presentation in calligraphic style for calligraphy works. At first, style words are generated by a clustering algorithm, and then Latent Style Model is builded to discover the calligraphic styles expressed by the collection of works. Finally, Kullback-Leibler distance is used to measure the style similarity between two calligraphy works.4. Calligraphy style relationships discovery. In order to discover the sytle relationships among artists, works and characters, two graph-based models are proposed: Supervised Learning Weighted Random Walk Model and Co-Random Walk Model. The main idea is as follows: constructing an entity-relationship graph according to low-level feature, context information and user interaction information firstly, and then using random walk model to measure the relationship between entities. The paper focuses on entity-relationship graph construction, edge weight learning, the usage of user interaction information and how to reduce the impact of user interaction on edge weight. Finally, several applications such as Calligraphy Style-Guided Works Browser, Style-Similar Calligraphy Works Retrieval, Author Identification for Works and Author Writing Style Analysis are built with different types of style relationships.5. Massive data retrieval and processing. In order to speed up the character retrieval, two efficient methods keypoint-based method and LSH-based method are proposed, and then a ReRank strategy is proposed to balance the tradeoff between retrieval speed and quality. Facing massive calligraphy resource in digital library, style modeling and relationships discovery algorithms mentioned above are implemented in the framework of MapReduce.