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We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order
This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple
into a high quality mosaic image. Second, we relax the conditions in the Maximum Clique based keyword detection algorithm.The new algorithm can handle watermark string with variant spacing. Therefore, not only English keywords, but also Japanese/Chinese keywords can be processed. The Experimental results show the
scenes by checking the discovered cross-media correlation. To make these two modalities comparable, photos related to the visited scenic spots are retrieved from image search engines, by the keywords extracted from text-based schedules. Sequences of key frames and retrieved photos are represented as visual word histograms
Overlaid text appears frequently in broadcast sports video. They provide supplementary information regarding the happenings of a particular game. Examples include important events of interest such as bookings and substitutions in a soccer match. Furthermore, overlaid-text is displayed when a particular concept of interest is happening or has happened. This paper presents a technique to automatically...
(page segmentation, keyword-spotting, optical character recognition (OCR), etc) are not yet as mature as for printed text. Thus, there is an imminent need to develop techniques to understand, archive, index and search the manuscripts. The antiquated approach of manually transcribing handwritten collections and then using
Word spotting systems are intended to retrieve occurrences of a given keyword in document images without actually recognizing the full document content. As there is a trend towards segmentation-free word spotting methods, we propose a methodology to evaluate these methods by employing measures that take the quality of
images amenable to browsing and searching in digital libraries. In this paper, we propose a novel multi-pass alignment method based on Hidden Markov Models (HMM) that combines text line recognition, string alignment, and keyword spotting to cope with word substitutions, deletions, and insertions in the transcription. In a
Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of
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
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