Kanji recognition in scene images without detection of text fields - robust against variation of viewpoint, contrast, and background texture

With the goal of indexing scene images, we propose a novel recognition method for Kanji characters captured in scene images. Our method scans multi-resolution images and classifies clipped regions with recognition dictionaries generated by learning a large amount of partial patterns of characters with large geometric transformation. The problem of scanning time, which tends to be unpractically long, is solved by using multi-compression coarse-to-fine scanning, and by detecting peak points after coarse searching. Despite the wrong results generated in the background, our method well supports image retrieval since it uses the regular spacing of characters. Experimental results show that this recognition method recognized characters at the rate of 82%. Precision was 84% and recall was 64% for image retrieval.