An automatic zip code recognition system for Japanese mail is proposed in this paper. It is assumed that a zip code is composed of three numerals and requited to be written in a specified frame. In actual images, however, the three numerals sometimes extend outside the specified frame and are not clearly separated. Considering this situation, the authors devised a system with two stages, the segmentation stage and the recognition stage. The segmentation stage consists of five steps: setting and adjusting of initial areas for numeral images (figures), calculation of the center of gravity of each figure, search for the horizontal and vertical boundaries of each figure, determination of the final area for each figure, and normalization of the figure in each final area. In the recognition stage, the Localized Arc Pattern Method (Arc method) proposed by Yoshimura et al. (1991) is implemented hierarchically; that is, a simple Arc method is applied first to each figure and a more complex one is applied subsequently unless the figure is identified in the first step. In the recognition process, every figure is judged as a numeral or otherwise rejected. The proposed system was applied to a database provided by the Institute for Post and Telecommunications Policy (IPTP). The segmentation algorithm yielded an adequate result. The recognition algorithm yielded scores as high as 90.6% in correct recognition rate and 0.7% in error rate. The best score of the precision index (P-index) specified by the IPTP was as low as 15.7 for the above mentioned IPTP database, while the score for another IPTP database was 16.9.

This paper proposes an off-line text-independent writer identification method applicable to Japanese and Korean sentences. It is assumed that the writer of a writing in question exists in a certain group of people and that reference writings written by each person in the group can be used for identification. In the proposed method, relative frequencies of some model patterns are counted on the binary pattern of each writing and are used as the feature to measure the distance between two writings. Based on a modified Mahalanobis' distance for this feature, the person whose reference writing is nearest to the writing in question is judged as the writer. The effectiveness of the proposed method is examined through an experiment using Japanese and Korean writings. Error rates in the experiment were different depending on conditions such as volume of reference writings, dimension of adopted features, and number of people to be identified. In some cases, error rates as low as 0% were observed. Error rates tend to be lower in Korean writings probably because Hangul is composed of a smaller number of letters compared to Kanji and Hiragana in Japanese writing.