• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1873-1880
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• 1997

It is an important factor to distinguish the kind of the character for increasing recognition rate before the character recognition in the document recognition system composed of the multi-font and multi-letters. All the letters of each country have a various unique characteristic in the each composition. In this paper, we used the stroke density as a method to distinguish the letter, and it has been adopted only Korean and English character. Input data is processed by the normalization to adopt multi-font document. Proposed method has been proved by the results of experiment the fact that the distinction probability of the Korean and English is more than 90%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.3
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• pp.55-59
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• 1984

This paper describes a method of Hangout recognition using 2 - D digital filtering. The 170 patterns classified by the positions of the initial sound (consonant), middle sound (vowel) and terminal sound (consonant) of the 1,659 characters were established and models formed by using 2 - D digital filtering for each patterns were obtained. Based on these models we proposed an algorithm that can recognize KOREAN combinational characters by separating patterns from them with superpostion principles. As a result of simulation, 100% of recognition rate is obtained in the case of the print letter.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• Journal of Oriental Neuropsychiatry
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• v.24 no.1
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• pp.27-44
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• 2013

Objectives : This study was designed to investigate the effects of Gastrodia elata Blume on the improvement of memory. Methods : This study was a 12 week, double blind, comparative clinical study. There were eligible who worked with a group of healthy seniors, all 60 years of age or older. 50 subjects were randomized either to Gastrodia elata Blume in powder form and steep in hot water or placebo. We measured the faculty of memory by using K-DRS, MMSE-K, Digit Span, Letter Fluency Test, Word List Memory Test, and the Trail Making Test, and after 12 weeks we measured the faculty of memory again using the same methods. Results : Gastrodia elata Blume steeps in the hot water group significantly increased. Initiation, perseveration level, and Memory level of K-DRS and MMSE-K score. There were no considerable differences between three groups in Digit Span and Trail Making Test score. Gastrodia elata Blume group showed significant advances in Letter Fluency Test and recognition of Word List Memory Test. Conclusions : The results suggest that Gastrodia elata Blume may have positive effects on memory improvement and function of the frontal lobe activation.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Child Studies in Asia-Pacific Contexts
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• v.4 no.1
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• pp.47-63
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• 2014

The purpose of this study was to develop a screening test for children at risk of reading underachievement and to investigate the effectiveness of the early-stage intervention program. In the first part of the study, we recruited 155 elementary first grade students for a screening test. Phonological deletion, digit naming, object naming, and sound-letter correspondence knowledge of a screening test, all assessed at the beginning of the school year, predicted the reading ability at the end of the school year. In the second part of the study, we analyzed the difference in the reading ability between fourteen children who participated in the intervention program and eighteen non-participating children. Reading ability was assessed by evaluating word recognition, oral reading fluency, reading comprehension, and pseudo-word recognition. The reading ability of intervention group improved more compared to control group, and the difference between two groups accentuated over time. However, final analysis conducted in November revealed that two groups did not differ significantly in oral reading fluency. This suggests that, unlike word recognition and comprehension, fluency might not dramatically improve in a short period.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.248-251
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• 2016

This thesis has discussed how to recognize and convert raised letters on tire to data and collect such data. Unlike the existing recognition system, the system presented by this thesis recognizes raised letters on tire through detecting letters after converting the Kinect camera image into image data in the preprocessing stage. After then, numbers and letters are analyzed through image improvement by use of binary images, noise filter, etc. In the recognition stage, letter distinction is used and raised letters on tire are recognized 100% through correction of errors by way of the correction algorithm for tire data recognition errors. In this paper it will be the development of a method of recognizing characters and the tire technology. Although there are many ways to the already recognized characters, Tire characters requires a technique different from the more general character recognition. For this reason and to develop additional technical methods and algorithms for character recognition.

• Journal of IKEEE
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• v.23 no.2
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• pp.642-651
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• 2019

One of the most important research topics on intelligent transportation systems in recent years is detecting and recognizing a license plate. The license plate has a unique identification data on vehicle information. The existing vehicle traffic control system is based on a stop and uses a loop coil as a method of vehicle entrance/exit recognition. The method has the disadvantage of causing traffic jams and rising maintenance costs. We propose to exploit differential image of camera background instead of loop coil as an entrance/exit recognition method of vehicles. After entrance/exit recognition, we detect the candidate images of license plate using the morphological characteristics. The license plate can finally be detected using SVM(Support Vector Machine). Letter and numbers of the detected license plate are recognized using CNN(Convolutional Neural Network). The experimental results show that the proposed algorithm has a higher recognition rate than the existing license plate recognition algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.8
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• pp.579-587
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• 1988

Conventional methods of automatic recognition of Korean characters consist of the thinning processing, the segmentation of connected fundamental phonemes and the recognition of each fundamental character. These methods, however require the thinning processing which is complex and time consuming. Also several noise components make worse effects on the recognition of characters than in the case of no thinning. This paper describes the extraction method of the feature components of Korean fundamental characters of the Gothic Korean letter without the thinning. We regard line-components of the contour which describes the character's external boundary as the feature-components. The line-component includes the directional code, the length and the start point in the image. Each fundamental character is represented by the string of directional codes. Therefore the recognition process is only the string pattern matching. We use the Gothic-hangul in the experiment. The ecognition rate is 92%.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.705-711
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• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.