• 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 %.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.456-459
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• 1999

In this paper, we propose a method of unconstrained handwritten numeral recognition using image dithering and multiple modular MLPs. The set of sample numeral patterns is subdivided into clusters which are extended by their radius. On each extended cluster, we constructed MLPs network as the expert recognizer of corresponding cluster. The gating network is also trained by an MLPs to weigh the outputs of expert MLPs. In training and test phase of the recognizer, we utilize the multiple dithered numeral images and the combination of the outputs for corresponding dithered images. Experimental results show that our recognition method works very well.

• Language and Information
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• v.10 no.2
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• pp.47-65
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• 2006

In this paper I address the problems of defining the semantics of numerals and accounting for how pragmatic inferences are made. I basically assume that a numeral n simply means '${\lambda}P{\lambda}x[#(x)n\;&\;P(x)]$', as commonly assumed. Even when a numeral n has 'at least' interpretation, a sentence with the number does not entail a sentence with n replaced with n-1. But when a sentence with n-1 holds, it is possible that a sentence with n or a larger number holds too. This is not based on a semantic relation, but on pragmatic informativeness. In addition to pragmatic strength, the actual reading of a numeral is affected by some background knowledge of generalizations about the world, but the ordering of pragmatic strength among numbers always plays a role in determining unilateral interpretations. In such a case, we can assume that a set of numbers relevant in the context forms a scale. Forming a scale does not necessarily lead to a unilateral interpretation. The bilateral interpretation of a number is possible in the context where it is known whether or not alternative sentences with contextually salient alternative numbers are true.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.685-688
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• 2000

In this paper, we used image information for the enhancement of Korean numeral speech recognition. First, a noisy environment was made by Gaussian generator at each 10 dB level and the generated signal was added to original Korean numeral speech. And then, the speech was analyzed to recognize Korean numeral speech. Speech through microphone was pre-emphasized with 0.95, Hamming window, autocorrelation and LPC analysis was used. Second, the image obtained by camera, was converted to gray level, autocorrelated, and analyzed using LPC algorithm, to which was applied in speech analysis, Finally, the Korean numerial speech recognition with image information was more ehnanced than speech-only, especially in ‘3’, ‘5’and ‘9’. As the same LPC algorithm and simple image management was used, additional computation a1gorithm like a filtering was not used, a total speech recognition algorithm was made simple.

• 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%.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.94-97
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• 2003

In this Paper, propose for MLP(multilayer perception) neural network that uses optimization recognition training scheme for the wavelet transform and the numeral image add to noise, and apply this system in Numeral Recognition. As important part of original image information preserves maximum using the wavelet transform, node number of neural network and the loaming convergence time did size of input vector so that decrease. Apply in training vector, examine about change of the recognition rate as optimization recognition training scheme raises noise of data gradually. We used original image and original image added 0, 10, 20, 30, 40, 50㏈ noise (or the increase of numeral recognition rate. In case of test image added 30∼50㏈, numeral recognition rate between the original image and image added noise for training Is a little But, in case of test image added 0∼20㏈ noise, the image added 0, 10, 20, 30, 40 , 50㏈ noise is used training. Then numeral recognition rate improved 9 percent.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.60-73
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• 2010

This paper aims to describe KorLexClas 1.5 which provides us with a very large list of Korean numeral classifiers, and with the co-occurring noun categories that select each numeral classifier. Differently from KorLex of other POS, of which the structure depends largely on their reference model (Princeton WordNet), KorLexClas 1.0 and its extended version 1.5 adopt a direct building method. They demand a considerable time and expert knowledge to establish the hierarchies of numeral classifiers and the relationships between lexical items. For the efficiency of construction as well as the reliability of KorLexClas 1.5, we use following processes: (1) to use various language resources while their cross-checking for the selection of classifier candidates; (2) to extend the list of numeral classifiers by using a shallow parsing techniques; (3) to set up the hierarchies of the numeral classifiers based on the previous linguistic studies; and (4) to determine LUB(Least Upper Bound) of the numeral classifiers in KorLexNoun 1.5. The last process provides the open list of the co-occurring nouns for KorLexClas 1.5 with the extensibility. KorLexClas 1.5 is expected to be used in a variety of NLP applications, including MT.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.1
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• pp.82-89
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• 2001

This paper presents recognition of unconstrained handwritten numeral strings using decision value generator, which is combined with both isolated digit identifier and recognizer designed with structural characteristics of digits. Numerical string recognition system is composed of three modules, which are pre-segmentation, segmentation and recognition. Pre-segmentation module classifies a numeral string into sub-images, which are isolated digit, touched digits or broken digit, using confidence value of decision value generator. Segmentation module segments touched digits using reliability value of decision value generator that will separate the leftmost digit from touched string of digits. Segmentation-based and segmentation-free methods have used for classification and segmentation, respectively. To evaluate proposed method, experiments have carried out with handwritten numeral strings of NIST SD19 and higher recognition performance than previous works has obtained with 96.7%.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.543-551
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• 1999

The primary purposes in this paper are to analyze the class separation of features in handwritten numeral recognition and to make use of the results in feature selection. Using the Parzen window technique, we compute the class distributions and define the class separation to be the overlapping distance of two class distributions. The dimension of a feature vector is reduced by removing the void or redundant feature cells based on the class separation information. The experiments have been performed on the CENPARMI handwritten numeral database, and partial classification and full classification have been tested. The results show that the class separation is very effective for the feature selection in the 10-class handwritten numeral recognition problem since we could reduce the dimension of the original 256-dimensional feature vector by 22%.

• Journal for History of Mathematics
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• v.25 no.4
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• pp.1-10
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• 2012

In Chinese mathematics, there have been two numeral systems, namely one in spoken language for recording and the other by counting rods for computations. They concerned with problems dealing with practical applications, numbers in them are concrete numbers except in the process of basic operations. Thus they could hardly develop a pure theory of numbers. In Song dynasty, 0 and TianYuanShu were introduced, where the coefficients were denoted by counting rods. We show that in this process, counting rods took over the role of the numeral system in spoken language and hence counting rod numeral system plays the role of that for abstract numbers together with the tool for calculations. Decimal fractions were also understood as denominate numbers but using the notions by counting rods, decimals were also admitted as abstract numbers. Noting that abacus replaced counting rods and TianYuanShu were lost in Ming dynasty, abstract numbers disappeared in Chinese mathematics. Investigating JianJie YiMing SuanFa(簡捷易明算法) written by Shen ShiGui(沈士桂) around 1704, we conclude that Shen noticed repeating decimals and their operations, and also used various rounding methods.