• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.373-380
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• 2008

So far there have been many researches on machine-based recognition of handwritten digit. But we have not yet attained the level of performance that can be satisfactory to men. The dissatisfaction with the performance of machine comes from not only the low accuracy of recognition but also the dissimilarity of the recognition results between man and machine. To reduce the difference of machine from man we first made an experiment with the human recognition of handwritten digits and then inquiry into the way of the human recognition that makes the results of men different from that of machine. We found out the attributes that play an important role in the human recognition process through the analysis of the experimental results like uni- and bi-directional confused pairs of digits, several ones unmixed up with another and the redundancy of mis-recognition, and proposed the approach direction to be able to improve the accuracy of the machine-based recognition, and furthermore the similarity in the recognition results of men and machine on the basis of the found facts above.

• 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 Intelligent Systems Conference
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• 1998.10a
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• pp.609-615
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• 1998

In this paper, we propose efficient total recognition system of handwritten and printed numerals for enhancing the classification time. The proposed system consist two step neuroclassifier: Printed numerals classifier and Handwritten numerals classifier. The performance of the propose classifier was tested on 5000 handwritten numerals database of NIST and 100 printed numerals database. In case of handwritten classifier, the overall classification times were 11 second. And in case of proposed system, the overall classification times were reduced by...

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.319-323
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• 2022

Handwritten text recognition is one of the active research areas nowadays. The progress in this field differs in every language. For example, the progress in Arabic handwritten text recognition is still insignificant and needs more attentions and efforts. One of the most important fields in this is Arabic handwritten manuscript text recognition which focuses in extracting text from historical manuscripts. For eons, ancients used manuscripts to write everything. Nowadays, there are millions of manuscripts all around the world. There are two main challenges in dealing with these manuscripts. The first one is that they are at the risk of damage since they are written in primitive materials, the second challenge is due to the difference in writing styles, hence most people are unable to read these manuscripts easily. Therefore, we discuss in this study different papers that are related to this important research field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• Journal of the Korean Graphic Arts Communication Society
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• v.6 no.1
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• pp.41-57
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• 1988

Recently, it Is required to develop OCR(Optical Character Reader) along with the progress of the information processing system for Hangeul. Characters have to be recognized clearly so that OCR can be applied, Structure analysis method and lump method are used for the recognition of characters, and OCR is now available for the recognition of printed characters and handwritten alphanumeric characters having simple structure by them However, It is known that there should be much more study on the development of handwritten Hangout's OCR. This paper proposed a new method for the handwritten Hangout character recognition. The units of Initial consonant of Hangout are separated and then recognized from the utilization of the position- Information of Hangeul's units from the normalized patterns using the regression line theory. It is carried out for the extraction of the block which exists in the virtual Initial consonant region from the normalized input patterns and the calculation on maximum value (${\beta}$) of likelihood after comparing the features of separated subpattern with the initial consonant dictionary.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.1-8
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• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• 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 the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.48-53
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• 2002

In this paper, we presented a new approach to the recognition of unconstrained handwritten numerals using an improved RBF(Radial Basis Function) Neural Networks. The RBF Neural Networks used Raised Cosine as a basis function to improve discrimination and reduce processing time. The performance of Raised Cosine RBF Neural Networks classifier was evaluated using totally unconstrained handwritten numeral database of Concordia University, Montreal, Canada, and the experimental results showed the recognition rate of 98.05%.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.577-580
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• 1997