We present a novel approach to dynamically generate high accuracy road maps through the statistical analysis of in- vehicle GPS trace data. Our approach opens a possibility to implement an optional system in a very economic way for mapping roads to support the applications in Intelligent Transportation Systems (ITS). The results presented in this paper show the potential with respect to the map update technology.

A camera based optical character reader (OCR) for Japanese Kanji characters was implemented on a mobile phone. This OCR has three key features. The first is discriminative feature extraction (DFE) which enables a character classifier needing only small memory size. The second is a word segmentation method specially designed for looking up Japanese words in a dictionary. The third feature is a GUI suitable for a mobile phone. A prototype mobile phone Kanji OCR was constructed and experimentally tested. Recognition accuracy of over 95% was obtained under the best conditions, which shows the potential of our prototype as a new type of electronic dictionary.

Gabor filter feature has been applied to character recognition but was not compared with the best direction feature: gradient feature. In this paper, we propose a principled method for implementing Gabor filters for character feature extraction and compare the recognition performances of Gabor feature and gradient feature on three databases. The results show that Gabor filters with low orientation sensitivity and broad frequency band favor recognition accuracy. The Gabor feature performs comparably or better than the gradient feature on two of the three databases, but is inferior on the rest one.

In this paper, we propose an approach to building compact classifier for camera-based printed Japanese character recognition on mobile phones. We design feature vector prototypes using learning vector quantization (LVQ) for achieving high accuracy, while the complexity is lowered by linear dimensionality reduction. The discriminative feature extraction (DFE) strategy, which optimizes both subspace axes and classifier parameters, is shown to yield high classification accuracy even on low dimensional subspace. On a 120D sub-space, a 4,344-class classifier consumes only 613KB storage, and an accuracy of 99.41% was obtained on a test set.

This paper describes a handwritten character string recognition system for Japanese mail address reading on a very large vocabulary. The address phrases are recognized as a whole because there is no extra space between words. The lexicon contains 111,349 address phrases, which are stored in a trie structure. In recognition, the text line image is matched with the lexicon entries (phrases) to obtain reliable segmentation and retrieve valid address phrases. The paper first introduces some effective techniques for text line image preprocessing and presegmentation. In presegmentation, the text line image is separated into primitive segments by connected component analysis and touching pattern splitting based on contour shape analysis. In lexicon matching, consecutive segments are dynamically combined into candidate character patterns. An accurate character classifier is embedded in lexicon matching to select characters matched with a candidate pattern from a dynamic category set. A beam search strategy is used to control the lexicon matching so as to achieve real-time recognition. In experiments on 3,589 live mail images, the proposed method achieved correct rate of 83.68 percent while the error rate is less than 1 percent.

We propose a new method to recognize the machine-printed monetary amount based on two-dimensional segmentation and bottom-up parsing. In conventional segmentation-based methods, the system segments the image only along the direction of the character line. This new method segments the image both horizontally and vertically, and extracts candidates of character segments correctly if there are many noises or characters are fragmented. A parsing module detects the optimal sequence of candidate segments using linguistic knowledge. In our method, a context-free grammar describes the linguistic constraints in the monetary amounts. We devised a new bottom-up parsing technique that interprets the results of character classification of the two-dimensionally segmented sub-images. We tested the validity of the new method using 1,314 images, and found that it improves the recognition accuracy significantly.

Proposes a handwritten character string recognition method for Japanese mail address reading on very large vocabulary. The recognition is performed by classification-embedded lexicon matching based on over-segmentation. The lexicon contains 111,349 address phrases and is represented in a trie structure. In recognition, the input text line image is matched with all lexicon entries by beam search to obtain reliable character segmentation and retrieve valid phrases. A classifier is embedded in lexicon matching to select from a dynamic set the characters matched with a candidate pattern. The beam search and the character classification jointly enable accurate phrase identification in real time. In experiments on 3,589 live mail images, the proposed method achieved correct rate of 83.68% with error rate less than 1%.

Describes a method for recognizing Japanese handwritten characters, including touching ones. The touching characters are segmented by cutting the connected components in a pre-segmentation process. The proposed segmentation method consists of the following steps: (1) identification of the touching components by checking the size of candidate character patterns; (2) estimation of the breakpoint and cutting of the components; and (3) generation of candidate character patterns by merging the components with adjacent patterns. The proposed method has the feature that the touching components can be identified even if they are comprised of relatively small patterns. The touching components are cut by using stroke width analysis and projection image analysis. This cut-and-merge procedure was able to segment 134 (88.2%) address lines correctly out of 152 Japanese address lines having touching portions, and could generate correct candidate character patterns.

A method of character segmentation of Japanese handwritten characters has been developed. It is effective especially in character recognition with the over-segmentation process. The method is based on the credibility measurement of each presegmented pattern for being a true character by analyzing peripheral features such as gaps between patterns and widths and heights of patterns. A heuristic statistical method is applied to the analysis. Experimental results show that the developed method increases the accuracy of character segmentation. It is effective especially when no linguistic feedback to segmentation is available and/or the character classifier ability is not high enough. In such cases, the recognition accuracy is increased from 30% to 70% by the new segmentation method.

Form document structure analysis is an essential technique for recognizing the positions of characters in general forms. However, it has a fundamental problem that interruptions of lines, as well as noise, lead to incorrect analysis. The paper focuses on a method for connecting junction patterns in which portions of the horizontal and vertical lines are not visible, referred to as "disappeared junction patterns". Our method has two key stages for making correct connections. The first is noise elimination, in which lines whose two end points meet no other lines and which are shorter than the minimum line length parameter, are eliminated. The second is object line selection, where only frame lines of tables are selected as object lines for connection. Experiments with 39 form images demonstrated the feasibility of this method.

Presents a new postprocessing method for recognizing handwritten Kanji names with Furigana, which shows the reading (pronunciation) of the Kanji in Kana characters, in real-time. Errors and rejects of Kanji OCR are automatically corrected to meaningful characters with the help of a Kanji-Furigana dictionary. Also, this method allows names to be written in any style. The method consists of Kanji word matching and Furigana verification. For Kanji word matching, Kanji name candidates are extracted. In Furigana verification, Kana candidate characters recognized by Kana OCR are compared with Furigana searched from the dictionary, using the Kanji name candidates. The correct name is selected from the results of Kanji and Kana word matching, plus the spacing between the first and second names. As result, 93.9% of the Kanji OCR's errors and rejects are corrected in real-time.>

A new, high speed method filling in the contours of alpha-numeric characters which produces accurate and correct image patterns is proposed. The method is called the improved edge fill method because it improves on a previously developed edge fill method. It does so by selecting fill pixels from contour lines expressed by Freeman's chain code and using combinations for neighboring the codes and accordingly processing time is reduced by about 20% over conventional methods. For binary image contour filling, the method eliminates faults found in conventional edge fill methods, and in consequence the areas inside contour lines are filled in rapidly and correctly. The method's effectiveness is examined in experiments using both Arabic numerals and letters of the Roman alphabet. As a result it is found that this filling method is able to produce correct and accurate image patterns and that it can be applied to alpha-numeric character contour filling.>

A fast algorithm for component labeling of binary images is proposed. Component labeling is an important method for separation of objects in document image understanding, especially in character and picture extraction. The proposed algorithm is based on sorting and tracking of runs and label propagation to the connected runs. Each scan line is partitioned into small segments, i.e. blocks and runs on the scan line are sorted according to the block sequence to speed up tracking of runs. It was ascertained by experiment that the processing time is 16.7 times faster than that of the conventional pixel-based labeling method.>