This paper presents a method to segment single- and multiple-touching characters in offline handwritten Japanese text recognition with practical speed. Distortions due to handwriting and a mix of complex Chinese characters with simple phonetic and alphanumeric characters leave optical handwritten text recognition (OHTR) for Japanese still far from perfection. Segmentation of characters, which touch neighbors on multiple points, is a serious unsolved problem. Therefore, we propose a method to segment them which is made in two steps: coarse segmentation and fine segmentation. The coarse segmentation employs vertical projection, stroke-width estimation while the fine segmentation takes a graph-based approach for thinned text images, which employs a new bridge finding process and Voronoi diagrams with two improvements. Unlike previous methods, it locates character centers and seeks segmentation candidates between them. It draws vertical lines explicitly at estimated character centers in order to prevent vertically unconnected components from being left behind in the bridge finding. Multiple candidates of separation are produced by removing touching points combinatorially. SVM is applied to discard improbable segmentation boundaries. Then, ambiguities are finally solved by the text recognition employing linguistic context and geometric context to recognize segmented characters. The results of our experiments show that the proposed method can segment not only single-touching characters but also multiple-touching characters, and each component in our proposed method contributes to the improvement of segmentation and recognition rates.

The paper presents a recognition method of character-position-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

This paper describes a significantly improved recognition system for on-line handwritten Japanese text free from line direction and character orientation constraints. The recognition system separates handwritten text of arbitrary character orientation and line direction into text line elements, estimates and normalizes character orientation and line direction, applies two-stage over-segmentation, constructs a segmentation-recognition candidate lattice and evaluates the likelihood of candidate segmentation-recognition paths by combining the scores of character recognition, geometric features and linguistic context. Enhancements over previous systems are made in line segmentation, over-segmentation and context integration model. The results of experiments on text from the HANDS-Kondate_t_bf-2001-11 database demonstrate significant improvements in the character recognition rate compared with the previous systems. Its recognition rate on text of arbitrary character orientation and line direction is now comparable with that possible on horizontal text with normal character orientation. Moreover, its recognition speed and memory requirement do not limit the platforms or applications that employ the recognition system.

The paper describes how a robust and compact on-line handwritten Japanese text recognizer was developed by compressing each component of an integrated text recognition system including a SVM classifier to evaluate segmentation points, an on-line and off-line combined character recognizer, a linguistic context processor, and a geometric context evaluation module to deploy it on hand-held devices. Selecting an elastic-matching based on-line recognizer and compressing MQDF2 via a combination of LDA, vector quantization and data type transformation, have contributed to building a remarkably small yet robust recognizer. The compact text recognizer covering 7,097 character classes just requires about 15 MB memory to keep 93.11% accuracy on horizontal text lines extracted from the TUAT Kondate database. Compared with the original full-scale Japanese text recognizer, the memory size is reduced from 64.1 MB to 14.9 MB while the accuracy loss is only 0.5% from 93.6% to 93.11%. The method is scalable so even systems of less than 11 MB or less than 6 MB still remain 92.80% or 90.02% accuracy, respectively.

Management of diet and exercise is especially significant in preventing "lifestyle-related diseases" for patients and subclinical cases. This paper introduces a questionnaire survey on diabetic regimens that targets 38 professional users such as physicians and nurses at a diabetic clinic. Based on the results of the questionnaire survey, a design concept for a wearable healthcare support system has been developed to provide patients with timely instruction in accordance with their current context. On the basis of this design concept, we developed a prototype of a wearable healthcare support system called "LifeMinder". "LifeMinder" is composed of a wristwatch-shaped wearable sensor module and a personal digital assistant (PDA). The sensor module measures 3-axis acceleration, pulse rate, galvanic skin reflex (GSR), and skin temperature. The PDA receives this data via BluetoothTM and recognizes the patient's general behavior such as "walking" or "eating". The recognition of these behaviors reduces the patient's mental and physical burden in daily healthcare and assists in support of medical treatment.