A CRF Based Scheme for Overlapping Multi-colored Text Graphics Separation
18 Sep 2011-pp 1215-1219
TL;DR: A novel framework for segmentation of documents with complex layouts performed by combination of clustering and conditional random fields (CRF) based modeling and has been extensively tested on multi-colored document images with text overlapping graphics/image.
Abstract: In this paper, we propose a novel framework for segmentation of documents with complex layouts. The document segmentation is performed by combination of clustering and conditional random fields (CRF) based modeling. The bottom-up approach for segmentation assigns each pixel to a cluster plane based on color intensity. A CRF based discriminative model is learned to extract the local neighborhood information in different cluster/color planes. The final category assignment is done by a top-level CRF based on the semantic correlation learned across clusters. The proposed framework has been extensively tested on multi-colored document images with text overlapping graphics/image.
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TL;DR: This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008 and provides a clear typology of documents and of document images segmentation algorithms.
Abstract: In document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community.
71 citations
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TL;DR: This paper tries to find a new way which can utilize existing methods to detect and extract text from born-digital image.
Abstract: The text appears in the images is important for fully understanding the images. The number of digital images and digital videos has increased tremendously. Although there are many methods have been proposed over the past years for the text extraction from natural scene images, the text detection and extraction from born-digital images are still a challenge. In this paper, we describe existing methods key ideas and try to summarize their advantages and disadvantages. We try to find a new way which can Comprehensive utilize existing methods to detect and extract text from born-digital image.
11 citations
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TL;DR: An approach to separate the non-texts from texts of a manuscript, mainly in the form of doodles and drawings of some exceptional thinkers and writers, and a computational approach to recover the struck-out texts to reduce human effort.
Abstract: In this paper we propose an approach to separate the non-texts from texts of a manuscript. The non-texts are mainly in the form of doodles and drawings of some exceptional thinkers and writers. These have enormous historical values due to study on those writers’ subconscious as well as productive mind. We also propose a computational approach to recover the struck-out texts to reduce human effort. The proposed technique has a preprocessing stage, which removes noise using median filter and segments object region using fuzzy c-means clustering. Now connected component analysis finds the major portions of non-texts, and window examination eliminates the partially attached texts. The struck-out texts are extracted by eliminating straight lines, measuring degree of continuity, using some morphological operations.
10 citations
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TL;DR: A consensus-based clustering approach for document image segmentation that is used iteratively with a classifier to label each primitive block and shows that the dependency of classification performance on the training data is significantly reduced.
Abstract: Segmentation of a document image plays an important role in automatic document processing. In this paper, we propose a consensus-based clustering approach for document image segmentation. In this method, the foreground regions of a document image are grouped into a set of primitive blocks, and a set of features is extracted from them. Similarities among the blocks are computed on each feature using a hypothesis test-based similarity measure. Based on the consensus of these similarities, clustering is performed on the primitive blocks. This clustering approach is used iteratively with a classifier to label each primitive block. Experimental results show the effectiveness of the proposed method. It is further shown in the experimental results that the dependency of classification performance on the training data is significantly reduced.
9 citations
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TL;DR: STRHOG, an extended version of HOG that is helpful for filtering spam images on cloud and a fair comparison with other methods, nearest neighbor classifier is used for the intelligent character recognition.
Abstract: Cloud storage has become an important way for data sharing in recent years. Data protection for data owner and harmful data filtering for data recipients are two non-negligible problems in cloud storage. Illegal or unsuitable messages on cloud have a negative impact on minors and they are easily converted into images to avoid text-based filtering. To detect the spam image with the embedded harmful messages on cloud, soft computing methods are required for intelligent character recognition. HOG, proposed by Dalal and Triggs, has been demonstrated so far to be one of the best features for intelligent character recognition. A pre-defined sliding window is always used for the generation of candidate character images when HOG is applied to recognize the whole word. However, due to the difference in character sizes, the pre-defined window cannot exactly match with each character. Variations on scale and translation usually occur in the character image to be recognized, which have a great influence on the performance of intelligent character recognition. Aiming to solve this problem, STRHOG, an extended version of HOG, is proposed in this paper. Experiments on two public datasets and one our dataset have shown encouraging results for our work. The improved intelligent character recognition is helpful for filtering spam images on cloud. To make a fair comparison with other methods, nearest neighbor classifier is used for the intelligent character recognition. It is expected that the performance should be further improved by using better classifiers such as fuzzy neural network.
5 citations
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TL;DR: The grating cell operator is the only one that selectively responds only to texture and does not give false response to nontexture features such as object contours and the texture detection capabilities of the operators are compared.
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TL;DR: It is demonstrated that by developing a visual vocabulary that explicitly represents the various aspects that distinguish one flower from another, it can overcome the ambiguities that exist between flower categories.
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714 citations
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