• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1032-1035
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• 2003

The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.986-991
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• 2002

This paper presents off-line handwritten numeral recognition method by using Eigen-Vectors. In this method, numeral features are extracted statistically by using Eigen-Vectors through KL transform and input numeral is recognized in the feature space by the nearest-neighbor classifier. In our feature extraction method, basis vectors which express best the property of each numeral type within the extensive database of sample numeral images are calculated, and the numeral features are obtained by using this basis vectors. Through the experiments with the unconstrained handwritten numeral database of Concordia University, we have achieved a recognition rate of 96.2%.

• Journal of IKEEE
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• v.19 no.4
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• pp.526-534
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• 2015

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.818-823
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• 2008

In this paper, we propose a feature extraction method which extracts directional features of handwritten numerals by using the projection runlength. Our directional featrures are obtained from four directional images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral shape respectively. A conventional method which extracts directional features by using Kirsch masks generates edge-shaped double line directional images for four directions, whereas our method uses the projections and their runlengths for four directions to produces single line directional images for four directions. To obtain the directional projections for four directions from a numeral image, some preprocessing steps such as thinning and dilation are required, but the shapes of resultant directional lines are more similar to the numeral lines of input numerals. Four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. By using a hybrid feature which is made by combining our feature with the conventional features of a mesh features, a kirsch directional feature and a concavity feature, higher recognition rates of the handwrittern numerals can be obtained. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the handwritten numeral database of Concordia University, we have achieved a recognition rate of 97.85%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1490-1499
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• 1999

This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.33-36
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• 2000

In this paper, we describe a study on the spotting and recognition of handwritten numerals using neural networks. To recognize a handwritten numeral, two kinds of neural network classifiers ate developed. One makes use of the positive samples only, while the other does both of the positive and negative samples. We propose two numeral spotters which discriminate between numerals and non-numerals. Those are also implemented by using neural networks. From the various experimental results, we found that our methods can be successfully applied to spot and recognize handwritten numerals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.912-915
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• 2002

This paper presents off-line handwritten numeral recognition method by using Eigen-Vectors. In this method, numeral features are extracted statistically by using Eigen-Vectors through KL transform and input numeral is recognized in the feature space by the nearest-neighbor classifier. In our feature extraction method, basis vectors which express best the property of each numeral type within the extensive database of sample numeral images are calculated, and the numeral features are obtained by using this basis vectors. Through the experiments with the unconstrained handwritten numeral database of Concordia University, we have achieved a recognition rate of 96.2%.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1031-1033
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• 2005

This paper presents a system of handwritten numerals recognition, which is based on Ant-miner algorithm (data mining based on Ant colony optimization). At the beginning, three distinct fractures (also called attributes) of each numeral are extracted. The attributes are Loop zones, End points, and Feature codes. After these data are extracted, the attributes are in the form of attribute = value (eg. End point10 = true). The extraction is started by dividing the numeral into 12 zones. The numbers 1-12 are referenced for each zone. The possible values of Loop zone attribute in each zone are "true" and "false". The meaning of "true" is that the zone contains the loop of the numeral. The Endpoint attribute being "true" means that this zone contains the end point of the numeral. There are 24 attributes now. The Feature code attribute tells us how many lines of a numeral are passed by the referenced line. There are 7 referenced lines used in this experiment. The total attributes are 31. All attributes are used for construction of the classification rules by the Ant-miner algorithm in order to classify 10 numerals. The Ant-miner algorithm is adapted with a little change in this experiment for a better recognition rate. The results showed the system can recognize all of the training set (a thousand items of data from 50 people). When the unseen data is tested from 10 people, the recognition rate is 98 %.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2761-2768
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• 2010

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by projection runlength, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our feature sets was tested by recognition experiments on the handwritten numeral database CENPARMI, where we used SVM with RBF kernel as a classifier. The experimental results showed that each combination of two or three features gave a better performance than a single feature. This means that each single feature works with a different discriminating power and cooperates with other features to enhance the recognition accuracy. By using the composite feature of the three features, we achieved a recognition rate of 98.90%.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.174-183
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• 2017

In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (${\theta}$), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages-Bangla and English. In general, we observed that our proposed contour features outperform other schemes.