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In this paper we propose a novel and efficient technique for finding keywords typed by the user in digitised machine-printed historical documents using the dynamic time warping (DTW) algorithm. The method uses word portions located at the beginning and end of each segmented word of the processed documents and try to
With large databases of document images available,a method for users to find keywords in documents will be useful. One approach is to perform Optical Character Recognition (OCR) on each document followed by indexing of the resulting text. However, if the quality of the document is poor or time is critical,complete OCR
very large when a dense grid is used where the histograms are computed and combined for many different points. The current dominating solution to this problem is to use a clustering method to create a visual codebook that is exploited by an appearance based descriptor to create a histogram of visual keywords present in an
list products based on keywords. As the inherent limitation, keyword browsing makes it difficult to find the exact products that human being desire. In this paper, we propose a visual search algorithm based on contour salient. The proposed approach extracts the object edge using Canny edge detector, and then chooses the
events in soccer video using on-screen texts. The proposed approach is completely automatic and independent to languages since it recommends the users to query events by keywords in image-form which are agents of clusters of stationary on-screen textboxes which are localized and extracted properly by a novel mechanism
collections was using keyword metadata, or simply by browsing. Nowadays, content based images retrieval (CBIR) is the way to assist the system to retrieve the related images. When the users are not satisfied with their query results, the relevance feedback (RF) retrieval is one of the solutions for this problem. The user needs
Curse of dimensionality is a major difficulty with the classic optimization methods for high dimensional applications in which the problem size grows rapidly and mostly exponential with the number of space. In this work we present a simple yet effective multi-agent approach to apply distributed particle swarm optimization to meet such demand. Lip detection in color images, as a high-dimensional problem,...
Photosketcher is an interactive system for progressively synthesizing novel images using only sparse user sketches as input. Photosketcher works on the image content exclusively; it doesn't require keywords or other metadata associated with the images. Users sketch the rough shape of a desired image part, and
beforehand. Approach here is to provide processed images to the Tesseract OCR to get better results than directly providing the raw video frames to the Tesseract OCR. The ticker text recognized can further be used for indexing of news videos on the basis of recognized keywords. Indexing of news videos is important for news
fail when only a tiny amount of labeled data is provided. In this paper, we propose QMAS (Querying, Mining And Summarization of Multi-modal Databases), a fast solution to the following problems: (i) low-labor labeling (L3) - given a collection of images, very few of which are labeled with keywords, find the most suitable
Content-based means that the search makes use of the contents of the images themselves, rather than relying on human inputted metadata such as captions or keywords. By content-based techniques, a user can specify contents of interest in a query. The contents may be colors, textures, shapes, or the spatial layout of
Managing and searching facsimiles automatically is the key point to achieve OA (Office Automation). At present, there is a lack of method to establish index of fax, which is the basis of searches. Focus on official business faxes, this paper proposes an approach to create index of fax, using logo, stamp and keywords
Existence of countless digital images has given rise to image retrieval in many applications. Conventional image databases being text-annotated pose two major problems of keywords for images and complexity. Hence, retrieval systems based on image's visual content are more desirable [1]. The content based image
method has an important characteristic that it can suggest multiple keywords per image, which improves the accuracy. Experimental results on pre-diagnosed database of brain images shows high accuracy (up to 95%), allowing us to claim that the use of associative classifier is an efficient technique to assist in the
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