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Recognizing characters in scene images suffering from perspective distortion is a challenge. Although there are some methods to overcome this difficulty, they are time-consuming. In this paper, we propose a set of affine-invariant features and a new recognition scheme called 'progressive removing' that can help reduce the processing time. Progressive removing gradually removes less feasible categories and skew angles by using multiple classifiers. We observed that progressive removing and the use of the affine invariant features reduced the processing time by about 60% in comparison to a trivial algorithm without decreasing the recognition rate. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 180(2): 55-63, 2012; Published online in Wiley Online Library (). DOI 10.1002/eej.22276 [ABSTRACT FROM AUTHOR]

In this paper, we propose a system for recommending tags for images. The proposed method presents to the user various tags with high precision by taking into account both the co-occurrence of tags and tags for similar images. Additional searches with user feedback enable us to find some new tags relevant to the image of interest. In order to test the proposed method, we compared it with a method based only on the co-occurrence of tags, as well as a method based only on the similarity of images. From the experimental results using 16,002 images, we have confirmed that the proposed method is capable of finding more tags compared to the methods used for comparison. We have also confirmed that the additional search is effective for finding additional tags. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(12): 57-64, 2011; Published online in Wiley Online Library (). DOI 10.1002/ecj.10342 [ABSTRACT FROM AUTHOR]

In order to make collaborative business activities fruitful, it is essential to know characteristics of organizations and persons in more details and to gather information relevant to the activities. In this paper, we describe a notion of “information recycling” that actualizes these requirements by analyzing documents. The key of recycling information is to utilize annotations on documents as clues for generating user profiles and for weighting contents in the context of the activities. We also propose a method of extracting annotations on paper documents just by pressing one button with the help of techniques of camera-based document image analysis. Experimental results demonstrate that it is fundamentally capable of acquiring annotations on paper documents on the condition that their electronic versions without annotations are available for the processing. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 165(2): 60–68, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20516 [ABSTRACT FROM AUTHOR]

Page segmentation is a process used to extract such components as columns, figures, tables, and photos from an image of a document. This article proposes a page segmentation technique that is stable, irrespective of component shape or tilted document image, based on analyzing the white region (background) of the document image. When we process a document that has non-rectangular and tilted components, the boundary of the components, that is, the white region, takes any shape. Thus, important questions include how to express white regions and how to process them. The proposed method uses thin lines that are extracted by thinning as an expression of white regions. Based on this expression of white regions, page segmentation is defined as extracting loops that surround the components. The proposed method extracts loops by eliminating unnecessary thin lines, for example, those that represent line spacing and character spacing. We try to use not only the feature of white regions, but also those of black regions, and to process several kinds of document layout. This paper examines the effectiveness and limitations of the proposed method based on experimental results that are taken from 80 sample images that are tilted from 0 to 45 degrees. © 1998 Scripta Technica. Syst Comp Jpn, 29(3): 59–68, 1998 [ABSTRACT FROM AUTHOR]

Post-processing of character recognition refers to the processing used to correct the errors in character recognition. When the input is a string representing a sentence in the highly precise error correction, it is desired that the syntactic as well as semantic examinations should be made at the sentence level. This paper assumes that the morphemes, syntax and semantics of the input sentence can be analyzed, and proposes a method that uses the syntactic and semantic analysis in the post-processing. The proposed method receives the list of candidate characters up to the fifth, and outputs the sentence that is adequate from the viewpoints of both syntax and semantics. The method features the following three points: (1) in word matching, it is examined also whether or not a sentence adequate from the viewpoints of syntax and semantics can be composed, and then the inadequate words extraction is inhibited; (2) characters having stronger syntactic and semantic constraints, such as the single-character particle and the conjugational suffix, are estimated top-down. Then, the case where the adequate character is not contained in the candidates can be handled; and (3) the words for which the adequateness cannot be determined from the syntactic or semantic viewpoint are selected by character re-recognition processing. An experiment is executed (or 50 sample sentences. The character recognition rate is improved from 83.0 percent to 98.0 percent, and the sentence recognition rate is improved from 10.0 percent to 94.0 percent. Compared to the method based only on word matching, the sentence recognition rate is improved by more than 20 percent. In other words, the effectiveness of the proposed method is demonstrated. [ABSTRACT FROM AUTHOR]

SUMMARY Specific object recognition methods based on the exact matching of feature vectors are known as methods that can achieve high recognition performance for large-scale three-dimensional specific object recognition. Since there are few common three-dimensional object datasets whose size is sufficient to explore the effect of differences in object dataset composition and the effect of increasing number of objects for recognition, these effects for specific object recognition methods based on exact matching of feature vectors have been discussed insufficiently. The number of objects in well-known datasets (e.g., COIL-100) is around 100. Therefore, in this research, we prepared a dataset of 1002 three-dimensional objects by themselves. In this paper, we discuss the effect of dataset differences, which are based on object structure, texture, and the number of objects, for methods such as that based on the Bloomier filter and that based on a hash table with this dataset in addition to COIL-100. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(9): 33-45, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11414 [ABSTRACT FROM AUTHOR]