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In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector. A 2- pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Added on September 8, 2017

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  • Contributed by : OCR onsortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : T Kasar, A G Ramakrishnan
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This paper describes a two level classification algorithm to discriminate the handwritten elements from the printed text in a printed document. The proposed technique is independent of size, slant, orientation, translation and other variations in handwritten text. At the first level of classification, we use two classifiers and present a comparison between the nearest neighbour classifier and Support Vector Machines(SVM) classifier to localize the handwritten text. The features that are extracted from the document are seven invariant central moments and based on these features, we classify the text as hand-written.

Added on September 8, 2017

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  • Contributed by : OCR Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : R. Kandan,Nirup Kumar Reddy ,K. R. Arvind ,A. G. Ramakrishnan
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This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of hand-written text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as hand-written using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text.

Added on September 8, 2017

7

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  • Contributed by : OCR Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Kandan Ramakrishnan,K.R Arvind,Ag Ramakrishnan
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Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-normalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid.

Added on September 8, 2017

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  • Contributed by : OCR Consortium
  • Product Type : Research Paper
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  • Author : K. R Arvind,Peeta Basa Pati,A.G. Ramakrishnan
Author Community Profile :

Writer independent handwriting recognition systems are limited in their accuracy, primarily due the large variations in writing styles of most characters. Samples from a single character class can be thought of as emanating from multiple sources, corresponding to each writing style. This also makes the inter-class boundaries, complex and disconnected in the feature space. Multiple kernel methods have emerged as a potential framework to model such decision boundaries effectively, which can be coupled with maximal margin learning algorithms. We show that formulating the problem in the above framework improves the recognition accuracy. We also propose a mechanism to adapt the resulting classifier by modifying the weights of the support vectors as well as that of the individual kernels. Experimental results are presented on a data set of 16,000 alphabets collected from 470 writers using a digitizing tablet.

Added on September 6, 2017

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  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
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  • System Requirement :
  • Author : Naveen Chandra Tewari ,Anoop M. Namboodiri