• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.3
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• pp.85-94
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• 1989

This paper proposes a method to recognize vowels and consonants in a handwritten Hangul text, in which the sizes of chracters and the spaces between characters are not uniform. In this method, all characters in the thinned image of a handwritten Hangul text are transformed into strokes, and the attributes which represent the relations between strokes are extracted from these strokes, and the attributes which represent the relations between strokes are extracted from these strokes. The vowels and consonants are recognized by applying attributed grammars to the strokes and attributes.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.161-166
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• 2004

Electronic Ink is a stored data in the form of the handwritten text or the script without converting it into ASCII by handwritten recognition on the pen-based computers and Personal Digital Assistants(PDA) for supporting natural and convenient data input. One of the most important issue is to search the electronic ink in order to use it. We proposed and implemented a script matching algorithm for the electronic ink. Proposed matching algorithm separated the input stroke into a set of primitive stroke using the curvature of the stroke curve. After determining the type of separated strokes, it produced a stroke feature vector. And then it calculated the distance between the stroke feature vector of input strokes and one of strokes in the database using the dynamic programming technique.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.87-94
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• 1999

This paper presents a study on the off-line handwritten Hangul (Korean) character recognition using the partially connected neural network (PCNN), which is based on partial connections between the input receptive fields and the hidden nodes. The hidden nodes of three PCNNs have ten receptive fields and different input feature sets. And we introduce modular partially connected neural network (MPCNN), The MPCNN combines three PCNNs with a merging network. The learning scheme of the proposed networks is composed of two steps: PCNN learning step and the merging step of combining three PCNN s. In the merging step, another merging PCNN network is introduced and trained by regarding the hidden output of each PCNN as a new input feature vector. The performance of the proposed classifier is verified on the recognition of 18 off-line handwritten Hangul characters widely used in business cards in Korea.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.1002-1013
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• 1996

The segmentation of characters is an important step in the automatic recognition of handwritten text. This paper proposes the segmenting method of off-line handwritten Hangul. The suggested approach is based on the structural characteristics of Hangul. The first step extracts the local features. connected component and strokes from the imput word. In the second step we identify the class of strokes. The third segmenting step specifies WRC(White Run Column) before consonant or horizontal vowel. If the segment is longer than threshold, the system estimates segmenting columns using the consonant-vowel information and column features, and then finds a cornered boundary along the strokes within the estimated segmenting columns.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.3
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• pp.50-59
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• 1993

There is basic problem with the current evaluation method for character recognition systems. The current method evaluates the average recognition rate by applying the test data to the target system. The average recognition rate tells no more than and no less than the overall performance and it depends on the data. In this paper we propose a testing method which will analyze the target system and point out its strong points and weak points. This can be made possible through using the data which are generated cy distorting the standard character images according to a carefully controlled manner. This paper will describe how to automatically generate such distorted images. Also we will show the method is actually effective and useful by applying it to evaluating existing recognition algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.1
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• pp.19-25
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• 2001

A novel approach for the feature extraction of handwritten characters is proposed by using spatial filtering neural networks with 4 layers. 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 system extracts feature information and removes the noises from feature information. The spatial filters adapted in the system correspond to the receptive fields of ganglion cells in retina and simple cells in visual cortex. With PE2 Hangul database, we perform experiments extracting features of handwritten characters recognition. It will be shown that the network can extract feature informations from handwritten characters successfully.

• 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.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.813-821
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• 2001

While recognition of isolated units of writing, such as a character or a word, has been extensively studied, emphasis on the segmentation itself has been lacking. In this paper we propose an active segmentation method for handwritten Hangul address strings based on the Run-length code. A slant correction algorithm, which is considered as an important preprocessing step for the segmentation, is presented. Three fundamental candidate estimation functions are introduced to detect the clues on touching points, and the classification of touching types is attempted depending on the structural peculiarity of Hangul. Our experiments show segmentation performance of 88.2% on touching characters with minimal over-segmentation.