Fast Image-based Chinese Calligraphic Character Retrieval on Large Scale Data

Chinese calligraphy is the art of handwriting, it draws a lot of attention for its beauty and elegance. In CADAL, a Calligraphic Character Dictionary (CCD) which contains hundreds of thousands of character images labeled with semantic meaning has been constructed and provided online to common users. It is a great challenge to perform quick and accurate image-based calligraphic character retrieval on CCD. In this paper, a novel shape descriptor, Oriented Shape Context (OSC) is proposed basing on the tranditional Shape Context (SC) to perform similarity searching. Together with GIST, GIST-OSC descriptor is proposed to represent calligraphic character image for efficient and effective retrieval. In addition, an effective retrieval schema is proposed. The retrieval schema works in two steps. Firstly approximate nearest neighbors of the query image are found quickly using GIST and then one-to-one fine matching between approximate nearest neighbors and the query image is performed using OSC. Our experiments show that the GIST-OSC descriptor and the retrieval schema are efficient and effective for Chinese calligraphic character retrieval on large scale data.