In recent years, various gesture recognition systems have been studied for use in television and video games[1].
In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning
popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have
become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is
unknown to the user during operation, which imposes several restrictions on his/her actions.
To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture
recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are
prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two
recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and
motion analysis using image frame differences (motion-based approach)(for example, see[4]).
We evaluated this system using sample users and classified the resulting errors into three categories: errors that
depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user
actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture
recognition systems.
Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In
this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of
processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained
jointly, rather than independently. In this way, common features can be shared across multiple classes for better
generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest
(ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and
two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a
large logo database demonstrate the effectiveness and efficiency of our proposed method.
Document management systems have become important because of the growing popularity of electronic filing of
documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading
on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the
electronic documents for document retrieval. Since texts generated by OCR generally include character recognition
errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval
method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion
of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation
errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other
character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher
than that of the conventional method. However, the precision rate was 64% lower.
Quality of camera-based whiteboard images is highly related to the light environment and the writing effect of the
content. Specular reflection and low contrast reduce the readability of captured whiteboard images frequently. A
novel method is proposed to enhance camera-based whiteboard images in this paper. The images are enhanced
by removing the highlight specular reflection to improve the visibility and emphasizing the content to improve
the readability of the whiteboards. The method can be practically embedded in mobile devices with image
capturing cameras.
KEYWORDS: Associative arrays, Optical character recognition, Feature extraction, Image processing, Scanners, Document management, Electronic imaging, Current controlled current source, Detection and tracking algorithms, Windows XP
Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical
Character Recognition (OCR) systems. To shorten processing time, recognition is usually split into separate preclassification
and recognition stages. For high overall recognition performance, the pre-classification stage must both
have very high classification accuracy and return only a small number of putative character categories for further
processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The
associative matching (AM) method has often been used for fast pre-classification, because its use of a hash table and
reliance solely on logical bit operations to select categories makes it highly efficient. However, redundant certain level of
redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data
on each axis and therefore does not take account of the distribution of the data. We propose a modified associative
matching method that satisfies the performance criteria described above but in a fraction of the time by modifying the
hash table to reflect the underlying distribution of training characters. Furthermore, we show that our approach
outperforms pre-classification by clustering, ANN and conventional AM in terms of classification accuracy,
discriminative power and speed. Compared to conventional associative matching, the proposed approach results in a
47% reduction in total processing time across an evaluation test set comprising 116,528 Japanese character images.
In recognizing characters written on forms, it often happens that characters overlap with pre-printed form lines. In order
to recognize overlapped characters, removal of the line and restoration of the broken character strokes caused by line
removal are generally conducted. But it is not easy to restore the broken character strokes accurately especially when the
direction of the line and the character stroke are almost same. In this paper, a novel recognition method of line-touching
characters without line removal is proposed in order to avoid the difficulty of the stroke restoration problem. A line-touching
character is recognized as a whole by matching with reference character features which include a line feature.
And the reference features are synthesized dynamically from a character feature and a line feature based on the touching
condition of an input line-touching character string. We compared the performance of the proposed method with a
conventional method in which a touching line is removed leaving the overlapped character stroke by mathematical
morphology. Experimental results show that proposed method can achieves 96.26% character recognition rate whereas
the conventional method achieves 92.77%.
One of the critical problems of an off-line handwritten character reader system is determining which patterns to read and which to ignore, as a form or a document contains not only characters but also spots and deletions. As long as they don't fit conditions for rejection, they cause recognition errors. Particularly, patterns of deleted single-character are difficult to be distinguished from a character, because their sizes are almost the same as that of a character and their shapes have variety. In this article, we proposed a method to detect such deletions in handwritten digits using topological and geometrical image- features suitable for detecting them; Eular number, pixel density, number of endpoint, maximum crossing counts and number of peaks of histogram. For precise detection, thresholds of the image features are adaptively selected according to their recognition results.
The global interpolation we proposed evaluates segment pattern continuity and connectedness to produce characters with smooth edges while interrupting blank or missing segments, e.g., in extracting a handwritten character overlapping one box border, correctly. In this paper, we expand our method to be able to separate handwritten characters overlapped a tabular formed slip. We solve two problems to realize it: (1) precise matching among blank segments of adjacent characters for interpolation, and (2) reinterpolation area decision when adjacent character strings are close to each other. Precise matching can be done by finding exact terminal points of blank segments or missing segments. We make efficient use of removed image in a border. The contour of the character segment in removed border image is tracked from the intersection of the character and the border toward the center of the border. Reinterpolation area is adaptively decided by not using one box border size, but, estimating a character size in each character string after removing borders of a tabular formed slip. When adjacent character strings are close to each other, their strings cannot be separated by calculating their horizontal projection value. We calculate the weighted horizontal projection value whose weight is approximated by a convex function, that is, the peak is in proportion to each labeled segment size and is set to the center of gravity of the labeled segment. Some experimental results show the effectiveness of our method.
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