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

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.63-74
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• 1996

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4632-4641
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• 2011

The detailed production traceability information is a fundamental element in successful introduction and revitalization of traceability system. In this paper, we propose a production traceability information gathering system which is based on handwritten data digitalization technology in agro-livestock products. By the proposed system, we can effectively gather the detailed production traceability information with digital pen and the management ledger of paper document type by only writing the ledger. The server of the system generates the same digital image as the ledger and converts the handwritten data into digital text to insert the data into the database. Because the system is superior to data gathering system based on PC, PDA and touch screen in mobility, usability, data input speed, suitability in agro-livestock environment, it is possible to effectively gather traceability information of high quality by users even if they have low information ability and insufficient time to input data. We expect that the handwritten data digitalization technology is used to gather document based information in stage of manufacturing, distribution and marketing. In addition, this technology is applied to implementing advanced traceability system with RFID/USN based systems.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3530-3538
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• 1999

In this paper, we propose a novel method for feature extraction of off-line handwritten characters recognition based on human optical neural field model. The proposed feature extraction system divide into three parts ; 1) smoothing process, 2) removing boundaries(boundary lines), 3) extracting feature information. The proposed system first removes rough pixels which are easy to occur in handwritten characters. The system then extracts and removes the boundary information which have no influence on characters recognition. Finally, the feature information for off-line handwritten characters recognition is extracted. With PE2 Hangul database, we perform feature extraction experiments for off-line handwritten characters recognition. In the experiment results, the proposed system based on optical neural field shows that can extract the feature information of off-line handwritten characters including curve lines, circles, quadrangles and so on.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.581-584
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• 1997

Discussed is stroke identification technique for automatic recognition of kanji characters without using the order of drawing strokes of a character.

• ETRI Journal
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• v.38 no.4
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• pp.703-713
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• 2016

In this paper, we present a segmentation-based method for offline Farsi handwritten word recognition. Although most segmentation-based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub-words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub-words. Through the extraction of the number of sub-words in each word, and labeling the position of each sub-word (beginning/middle/end), many of the sub-word classifiers can be pruned, and a few remaining sub-word classifiers can be evaluated during the sub-word recognition stage. The candidate sub-words are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.385-392
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• 2006

This paper proposes a new stroke extraction algorithm for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The proposed algorithm extracts vertical, slant, and horizontal strokes from phonemes using run-length. The run-length of vertical or slant strokes becomes the width, and also the number of horizontal run-lengths the width. After extracting horizontal strokes from phonemes, the algorithm links two continuous vertical or slant stokes with run-lengths of the strokes' width to represent the features of a character. The extracted strokes can be utilized to recognize a character, using template matching of strokes, which is being adopted in on-line handwritten Hangul recognition.

• 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%.

• Smart Media Journal
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• v.1 no.4
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• pp.59-71
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• 2012

Since mobile phones are used as common communication devices, their applications are increasingly important to human's life. Using smart-phones camera to collect daily life environment's information is one of targets for many applications such as text recognition, object recognition or context awareness. Studies have been conducted to provide important information through the recognition of texts, which are artificially or naturally included in images and movies acquired from mobile phones. In this study, a character segmentation method that improves character-recognition accuracy in images obtained from mobile phone cameras is proposed. The proposed method first classifies texts in a given image to printed letters and handwritten letters since segmentation approaches for them are different. For printed letters, rough segmentation process is conducted, then the segmented regions are integrated, deleted, and re-segmented. Segmentation for the handwritten letters is performed after skews are corrected and the characters are classified by integrating them. The experimental result shows our method achieves a successful performance for both printed and handwritten letters as 95.9% and 84.7%, respectively.

• 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%.