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In this paper, a novel fast logo detection approach in document images is presented. Logos with separated parts usually can affect the logo detection process. To overcome this problem, some specifications of logos are considered. Our proposed method divided in three main sections. In the first section, a horizontal dilation operator is used to merge separated parts of logo in horizontal direction...
This paper presents a system for the optimization of text recognition algorithms. First a theoretic four-staged model of text recognition is proposed. In this four-staged model, the second stage called text localization is optimized. A reinterpreted version of the F measure is used as a fitness indicator for optimization of the localization. The optimization method is described and the role of the...
The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial...
A novel method is proposed in this paper to detect texts from scene images captured by digital cameras. It converts the text detection problem to a shape classification problem by means of the topographic maps, and performs shape classification by exploiting the over-complete and sparse structure in the shape data. Finally, layout analysis is applied to complete the text line detection. The proposed...
The paper presents a method for efficient text detection in unconstrained environments, based on image features derived from connected components and on a classification architecture implementing a focus of attention approach.The main application motivating the work is container code detection with the final goal of checking freight trains composition. Although the method is strongly influenced by...
In this paper, we propose a new method based on wavelet transform, statistical features and central moments for both graphics and scene text detection in video images. The method uses wavelet single level decomposition LH, HL and HH subbands for computing features and the computed features are fed to k means clustering to classify the text pixel from the background of the image. The average of wavelet...
An algorithm for text detection in images from street view, basing on Haar-like features and AdaBoost classification is proposed in this paper. The idea is intended for searching a wanted place in a foreign city with the business name, the scene text and the street address. There are two contributions in this paper. First the difficulty of locating the specified buildings exactly in an unfamiliar...
Object recognition based on probabilistic Latent Semantic Analysis (pLSA) has shown excellent performance, but it is sensitive to background clutter. In this paper, we propose a novel framework called AM-pLSA, which combines pLSA with visual attention model, to learn object classes from unlabeled images with cluttered background. We firstly detect salient regions and non-salient regions in an image...
The starting and ending (S & E) frames of each text that embeds in a scene or is graphically added to video, provide not only important clues for highlight events detection in semantic-based video analysis, indexing and retrieval but also hint for decoding the videos structure and classification. In this paper, a fast text tracking method of determining S & E frames is proposed, which consists...
When implemented in hardware, image-processing algorithms should be robust to memory limitations because some hardware architectures may not have memory size as large as the whole frame size. Although this is not generally a problem for low-level processing, higher-level understanding, such as object detection, demands novel solutions because the available information may, in some cases, be very local,...
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