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Recent Advances in Handwriting Recognition
- DOCUMENT ANALYSIS, EDITORS: UMAPADA PAL, SWAPAN K. PARUI, BIDYUT B. CHAUDHURI, PP.1-30
"... Machine simulation of human reading has been subject of intensive research for the last three decades. This paper presents a summary about the recent advances in terms of character, word, numeral string, and setence recognition. In addition, the main new trends in the field of handwriting recognitio ..."
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Machine simulation of human reading has been subject of intensive research for the last three decades. This paper presents a summary about the recent advances in terms of character, word, numeral string, and setence recognition. In addition, the main new trends in the field of handwriting recognition are discussed and some important contributions are presented.
Aires, Handwritten Character Recognition Using Nonsymmetrical Perceptaul Zoning
- International Journal of Pattern Recognition and Artificial Intelligence
, 2007
"... In this paper we present an alternative strategy to define zoning for handwriting recog-nition, which is based on nonsymmetrical perceptual zoning. The idea is to extract some knowledge from the confusion matrices in order to make the zoning process less empirical. The feature set considered in this ..."
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In this paper we present an alternative strategy to define zoning for handwriting recog-nition, which is based on nonsymmetrical perceptual zoning. The idea is to extract some knowledge from the confusion matrices in order to make the zoning process less empirical. The feature set considered in this work is based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. To better assess the nonsymmetrical zoning we carried out experiments using four dif-ferent zonings strategies. Experiments show that the nonsymmetrical zoning could be considered as a tool to build more reliable handwriting recognition systems.
Metaclasses and Zoning Mechanism Applied to Handwriting Recognition
"... Abstract: The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. The features considered in this work are based on concavities/convexities deficiencies, which are obt ..."
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Abstract: The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. The features considered in this work are based on concavities/convexities deficiencies, which are obtained by labelling the background pixels of the input image. Four different perceptual zoning (symmetrical and non-symmetrical) are discussed. Experiments show that this mechanism of zoning could be considered as a reasonable alternative to exhaustive search algorithms. The second contribution is a methodology to define metaclasses for the problem of handwritten character recognition. The proposed approach is based on the disagreement among the characters and it uses Euclidean distance computed between the confusion matrices. Through comprehensive experiments we demonstrate that the use of metaclasses can improve the performance of the system.
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
"... Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant rem ..."
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Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60 % to 94 % using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples. Keywords—On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates I.
unknown title
"... Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant rem ..."
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Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60 % to 94 % using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples. Keywords—On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates I.
unknown title
"... Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant rem ..."
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Abstract—On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60 % to 94 % using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples. Keywords—On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates I.
Zoning and Metaclasses for Character Recognition
"... ABSTRACT The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. The features considered in this work are based on concavities/convexities deficiencies, which are obt ..."
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ABSTRACT The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. The features considered in this work are based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. Four different perceptual zoning (symmetrical and non-symmetrical) are discussed. Experiments show that this mechanism of zoning could be considered as a reasonable alternative to exhaustive search algorithms. The second contribution is a methodology to define metaclasses for the problem of handwritten character recognition. The proposed approach is based on the disagreement among the characters and it uses Euclidean distance computed between the confusion matrices. Through comprehensive experiments we demonstrate that the use of metaclasses can improve the performance of the system.
Zoning and Metaclasses Improving the Character Recognition
"... The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. We will see that this idea can be used as a tool to support the design of zoning. The features considered in th ..."
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The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. We will see that this idea can be used as a tool to support the design of zoning. The features considered in this work are based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. Four different perceptual zoning (symmetrical and nonsymmetrical) are discussed. Experiments show that this mechanism of zoning could be considered as a reasonable alternative to exhaustive search algorithms. The second contribution is a methodology to define metaclasses for the problem of handwritten character recognition. The proposed approach is based on the disagreement among the characters and it uses Euclidean distance computed between the confusion matrices. Through comprehensive experiments we demonstrate that the use of meta-classes can improve the performance of the character recognition system.
Author manuscript, published in "International Conference on Frontiers in Handwriting Recognition (2010)" Handwritten word verification by SVM-based hypotheses re-scoring and multiple thresholds rejection
, 2010
"... In the field of isolated handwritten word recognition, the development of verification systems that optimize the trade-off between performance and reliability is still an active research topic. To minimize the recognition errors, usually, a verification system is used to accept or reject the hypothe ..."
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In the field of isolated handwritten word recognition, the development of verification systems that optimize the trade-off between performance and reliability is still an active research topic. To minimize the recognition errors, usually, a verification system is used to accept or reject the hypotheses output by an existing recognition system. In this paper, a novel verification architecture is presented. In essence, the recognition hypotheses, re-scored by a set of the support vector machines, are validated by a verification mechanism based on multiple rejection thresholds. In order to tune these (classdependent) rejection thresholds, an algorithm based on dynamic programming is proposed which focus on maximizing the recognition rate for a given prefixed error rate. Preliminary reported results of experiments carried out on RIMES database show that this approach performs equal or superior to other state-of-the-art rejection methods. 1.
