We propose a novel background and foreground estimation algorithm in MAP-MRF approach for binarization of degraded document image. In the proposed algorithm, an assumption that background whiteness and foreground blackness is not employed differently from the conventional algorithm, and we employ character's irregularities based on local statistics. This makes the method possible to apply to the image with various colored characters, ex. outlined characters by colored background. The effectiveness and the validity are shown by applying the proposed method to various degraded document images.

This paper describes COCE (COordinative Character Extractor), a method for extracting printed Japanese characters and their character strings from all sorts of document images. COCE is based on a multi-agent system where each agent tries to find a character string and extracts the characters in it. For the adaptability, the agents are allowed to look after arbitrary parts of documents and extract the characters using only the knowledge independent of the layouts. Moreover, the agents check and correct their results sometimes with the help of the other agents. From experimental results, we have verified the effectiveness of our approach.

In this paper, we present a method for extracting the Japanese printed characters from unformatted document images. This research takes into account the multiple general features specific to the Japanese printed characters. In our method, these features are thought of as the constraints for the regions to be extracted within the constraint satisfaction approach. This is achieved by minimizing a constraint function estimating quantitative satisfaction of the features. Our method is applicable to all kinds of the Japanese documents because it is no need of a priori knowledge about the document layout. We have favorable experimental results for the effectiveness of this method.

The character binarization method MTC is developed for enhancing the recognition of characters in general outdoor images. Such recognition is traditionally difficult because of the influence of illumination changes, especially strong shadow, and also changes in character, such as apparent character sizes. One way to overcome such difficulties is to restrict objects to be processed by using strong hypotheses, such as type of object, object orientation and distance. Several systems for automatic license plate reading are being developed using such strong hypotheses. However. their strong assumptions limit their applications and complicate the extension of the systems. The MTC method assumes the most reasonable hypotheses possible for characters: they occupy plane areas, consist of narrow lines, and external shadow is considerably larger than character lines. The first step is to eliminate the effect of local brightness changes by enhancing feature including characters. This is achieved by applying mathematical morphology by using a logarithmic function. The enhanced gray-scale image is then binarized. Accurate binarization is achieved because local thresholds are determined from the edges detected in the image. The MTC method yields stable binary results under illumination changes, and, consequently, ensures high character reading rates. This is confirmed with a large number of images collected under a wide variety of weather conditions. It is also shown experimentally that MTC permits stable recognition rate even if the characters vary in size.