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.

This letter has retrained an MQDF classifier on the retraining set, which is constructed by samples locating near classification boundary. The method is evaluated on HCL2000 and HCD Chinese handwriting sets. The results show that the retrained MQDF outperforms MQDF and cascade MQDF on all test sets.

To enhance safety and traffic efficiency, a driver assistance system and an autonomous vehicle system are being developed. A preceding vehicle recognition method is important to develop such systems. In this paper, a vision-based preceding vehicle recognition method, based on supervised learning from sample images is proposed. The improvement for Modified Quadratic Discriminant Function (MQDF) classifier that is used in the proposed method is also shown. And in the case of road environment recognition including the preceding vehicle recognition, many researches have been reported. However in those researches, a quantitative evaluation with large number of images has rarely been done. Whereas, in this paper, over 1,000 sample images for passenger vehicles, which are recorded on a highway during daytime, are used for an evaluation. The evaluation result shows that the performance in a low order case is improved from the ordinary MQDF. Accordingly, the calculation time is reduced more than 20% by using the proposed method. And the feasibility of the proposed method is also proved, due to the result that the proposed method indicates over 98% as classification rate.