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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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  • 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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  • 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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