Topic
Intelligent word recognition
About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.
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Papers
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TL;DR: Three techniques with two different conventional segmentation algorithms in conjunction with backpropagation and radial basis function neural networks have been used in this research to create a novel Borda count for fusion based on ranks and confidence values.
46 citations
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23 Sep 2007TL;DR: This paper describes character based elastic matching using local features for recognizing online handwritten data using Dynamic time warping with four different feature sets containing x-y, normalized first and second derivatives and curvature features and proposed a 2-stage recognition scheme.
Abstract: This paper describes character based elastic matching using local features for recognizing online handwritten data. Dynamic time warping (DTW) has been used with four different feature sets: x-y features, shape context (SC) and tangent angle (TA) features, generalized shape context feature (GSC) and the fourth set containing x-y, normalized first and second derivatives and curvature features. Nearest neighborhood classifier with DTW distance was used as the classifier. In comparison, the SC and TA feature set was found to be the slowest and the fourth set was best among all in the recognition rate. The results have been compiled for the online handwritten Tamil and Telugu data. On Telugu data we obtained an accuracy of 90.6% with a speed of 0.166 symbols/sec. To increase the speed we have proposed a 2-stage recognition scheme using which we obtained accuracy of 89.77% but with a speed of 3.977 symbols/sec.
46 citations
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09 May 1999TL;DR: The formation of a comprehensive database of handwritten Arabic words, numbers, and signature, for use in optical character recognition research related to the Arabic language is described.
Abstract: This paper describes the formation of a comprehensive database of handwritten Arabic words, numbers, and signature, for use in optical character recognition research related to the Arabic language. So far no such (freely or commercially available) database exists.
45 citations
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01 Oct 1997TL;DR: A new off-line word recognition system that is able to recognize unconstrained handwritten words using grey-scale images based on structural and relational information in the handwritten word is presented.
Abstract: In this paper, we present a new off-line word recognition system that is able to recognize unconstrained handwritten words using grey-scale images. This is based on structural and relational information in the handwritten word. We use Gabor filters to extract features from the words, and then use an evidence-based approach for word classification. A solution to the Gabor filter parameter estimation problem is given, enabling the Gabor filter to be automatically tuned to the word image properties. We also developed two new methods for correcting the slope of the handwritten words. Our experiments show that the proposed method achieves good recognition rates compared to standard classification methods.
45 citations
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13 Dec 2008TL;DR: In this paper, a system for offline recognition of handwritten handwritten Tamil characters using Hidden Markov Models (HMM) has been presented, which uses a combination of Time domain and frequency domain feature.
Abstract: Concerning to optical character recognition, handwriting has sustained to persist as a means of communication and recording information in day to day life even with the introduction of new technologies. Hidden Markov Models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. A system for offline recognition of cursive handwritten Tamil characters is presented. In this effort, offline cursive handwritten recognition system for Tamil based on HMM and uses a combination of Time domain and frequency domain feature is proposed. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.
45 citations