Koji Kurokawa

A Multi-Stage Strategy to Perspective Rectification for Mobile Phone Camera-Based Document Images

Document images captured by a mobile phone camera often have perspective distortions. Efficiency and accuracy are two important issues in designing a rectification system for such perspective documents. In this paper, we propose a new perspective rectification system based on vanishing point detection. This system achieves both the desired efficiency and accuracy using a multi-stage strategy: at the first stage, document boundaries and straight lines are used to compute vanishing points; at the second stage, text baselines and block aligns are utilized; and at the last stage, character tilt orientations are voted for the vertical vanishing point. A profit function is introduced to evaluate the reliability of detected vanishing points at each stage. If vanishing points at one stage are reliable, then rectification is ended at that stage. Otherwise, our method continues to seek more reliable vanishing points in the next stage. We have tested this method with more than 400 images including paper documents, signboards and posters. The image acceptance rate is more than 98.5% with an average speed of only about 60 ms.

Highly accurate retrieval method of Japanese document images through a combination of morphological analysis and OCR

We have developed a method that allows Japanese document images to be retrieved more accurately by using OCR character candidate information and a conventional plain text search engine. In this method, the document image is first recognized by normal OCR to produce text. Keyword areas are then estimated from the normal OCR produced text through morphological analysis. A lattice of candidate- character codes is extracted from these areas, and then character strings are extracted from the lattice using a word-matching method in noun areas and a K-th DP-matching method in undefined word areas. Finally, these extracted character strings are added to the normal OCR produced text to improve document retrieval accuracy when u sing a conventional plain text search engine. Experimental results from searches of 49 OHP sheet images revealed that our method has a high recall rate of 98.2%, compared to 90.3% with a conventional method using only normal OCR produced text, while requiring about the same processing time as normal OCR.

A Learning Pseudo Bayes Discriminant Method Based on Difference Distribution of Feature Vectors

We developed a learning pseudo Bayes discriminant method, that dynamically adapts a pseudo Bayes discriminant function to a font and image degradation condition present in a text. In this method, the characteristics of character pattern deformations are expressed as a statistic of a difference distribution, and information represented by the difference distribution is integrated into the pseudo Bayes discriminant function. The formulation of integrating the difference distribution into the pseudo Bayes discriminant function results in that a covariance matrix of each category is adjusted based on the difference distribution. We evaluated the proposed method on multifont texts and degraded texts such as compressed color images and faxed copies. We found that the recognition accuracy of our method for the evaluated texts was much higher than that of conventional methods.

Fast precise preclassification method using a candidate table designed by projections of feature regions

For feasible recognition having many categories such as Japanese character recognition, fast matching algorithms are necessary because the matching process occupies most of recognition time. In addition, for improving recognition accuracy, the matching process must use more complicated discrimination functions or a higher dimensional feature space, which involves higher computational costs. Therefore, pre-classification is used, which outputs a set of candidate categories to decrease the number of computations of the complicated discrimination functions.