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

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Added on September 6, 2017

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  • Author : Rituraj Kunwar,A. G. Ramakrishnan
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In this work, we propose an online handwriting solution, where the data is captured with the help of depth sen¬sors. Users may write in the air and our method recognizes it in real time using the proposed feature representation. Our method uses an efficient fingertip tracking approach and reduces the necessity of pen up/pen-down switching. We validate our method on two depth sensors, Kinect and Leap Motion Controller. On a dataset collected from 20 users, we achieve a recognition accuracy of 97.59% for character recognition. We also demonstrate how this system can be extended for lexicon recognition with reliable performance. We have also prepared a dataset containing 1,560 characters and 400 words with the intention of providing common benchmark for handwritten character recognition using depth sensors and related research.

Added on September 6, 2017

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  • Author : Rajat Aggarwal,Sirnam Swetha,Anoop M. Namboodiri,Jayanthi Sivaswamy,C. V. Jawahar