Ryuji Mine

Camera-based Kanji OCR for mobile-phones: practical issues

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.

Building compact classifier for large character set recognition using discriminative feature extraction

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.

Lexical Search Approach for Character-String Recognition

A novel method for recognizing character strings, based on a lexical search approach, is presented. In this method, a character string is recognized by searching for a sequence of segmented patterns that fits a string in a lexicon. A remarkable characteristic of this method is that character segmentation and character classification work as subfunctions of the search. The lexical search approach enables the parameters of character classifier to adapt to each segmented pattern. As a result, it improves the recognition accuracy by omitting useless candidates of character classification and by changing the criterion of rejection dynamically. Moreover, the processing time is drastically reduced by using minimum sets of categories for each segmented pattern. The validity of the developed method is shown by the experimental results using a lexicon including 44,700 character strings.

A recognition method of machine-printed monetary amounts based on the two-dimensional segmentation and the bottom-up parsing

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.