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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
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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
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  • Author : Naveen Chandra Tewari ,Anoop M. Namboodiri

Research in the field of recognizing unlimited vocabulary, online handwritten Indic words is still in its infancy. Most of the focus so far has been in the area of isolated character recognition. In the context of lexicon-free recognition of words, one of the primary issues to be addressed is that of segmentation. As a preliminary attempt, this paper proposes a novel script-independent, lexicon free method for segmenting online handwritten words to their constituent symbols. Feedback strategies, inspired from neuroscience studies, are proposed for improving the segmentation. The segmentation strategy has been tested on an exhaustive set of 10000 Tamil words collected from a large number of writers. The results show that better segmentation improves the overall recognition
performance of the handwriting system.

Added on September 6, 2017

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  • Contributed by : OHWR Consortium
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  • Author : Suresh Sundaram, A G Ramakrishnan
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For automatic recognition of Bangla script, only a few studies are reported in the literature, which is in contrast to the role of Bangla as one of the world’s major scripts. In this paper we present a new approach to online Bangla handwriting recognition and one of the first to consider cursively written words instead of isolated characters. Our method uses a sub stroke level feature representation of the script and a writing model based on hidden Markov models.

Added on September 6, 2017

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  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
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  • Author : Gernot A. Fink,Szilard Vajda,Ujjwal Bhattacharya,Swapan K. Parui,Swapan K. Parui
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This work describes the development of online handwritten isolated Bengali numerals using Deep Autoencoder (DA) based on Multilayer perceptron (MLP) [1]. Autoencoders capture the class specific information and the deep version uses many hidden layers and a final classification layer to accomplish this. DA based on MLP uses the MLP training approach for its training. Different configurations of the DA are examined to find the best DA classifier.

Added on September 6, 2017

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  • Contributed by : OHWR Consortium
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  • Author : Arghya Pal,Vineeth N Balasubramanian,B. K. Khonglah,S. Manda,Himakshi Choudhury,S. R. M. Prasanna,H. L. Rufiner