• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1379-1389
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• 1996

Hangul character always consists of consonants-vowel-consonants in order. Using this point, this paper proposes an approach to design a model for spotting each letter in Hangul, and then recognize characters based on the spotting results. The network model consist of a set of HMMs. The letter search is carried out by Viterbi algorithm, while character recognition is performed by searching the lattice of letter hypotheses. Experimental results show that, in spite of simple architecture of recognition, the performance is quite high reaching 87.47% for discrete regular characters. In particular the approach shows highly plausible segmentation of letters in characters.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.427-430
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• 1995

This paper proposes a new image processing algorithm to recognize korean documents. It take out the region of syllable area from input character image, then it makes recognition of a consonant and a vowel in the character. A precision segmentation is very important to recognize the input character. The input image has 8-bit gray scaled resolution. Not only the shape but also vertical and horizontal lines dispersion graph are used for segmentation. Theresult shows a higher accuracy of character segmentation.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.99-115
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• 2021

Unlike most other alphabetic languages, letter transposition effect was not found in Korean except in the syllable level and in the morpheme level. This study was conducted in order to investigate the possible reason of the absence of letter transposition effect in Korean. Based on previous letter transposition studies, this study was to investigate on whether syllable frequency is a moderating variable and is responsible for the absence of the letter transposition effect. The results showed that significant letter transposition effect was found when a transposed non-word has high frequency syllable(e.g., 민주화 → 진무화), while such effect was not seen in a transposed non-word with low frequency syllable. The results showed that the letter transposition effect can found in Korean as well. The results also implicate the possibility that syllable frequency is the main moderating variable regarding the Korean letter transposition effect.

• Korean Journal of Cognitive Science
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• v.25 no.1
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• pp.1-24
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• 2014

Growing boy of studies focus on the letter transposition effect since it gives the information on how letters are coded and what variables are involved in the processes of word recognition. This review investigated various models on letter transposition effect. While most proposed models rely mainly on the bottom-up processes, evidences from various studies suggested the necessity of the top-down variables based on the cognitive processing mechanism. Especially, empirical evidences suggested that Hangul model should include a position specific processing mechanism based on onset, vowel, and coda of the Korean character.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.1-9
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• 2003

In implicit learning tasks, human participants learn grammatical letter strings better than random letter strings. After learning grammatical letter strings, participants were able to judge the grammaticality of new letter strings that they have never seen before. EPAM (Elementary Perceiver and Memorizer) IV, a rote learner without any rule abstraction mechanism, was used to simulate these results. The results showed that EPAM IV with a within-item chunking function was able to learn grammatical letter strings better than random letter strings and discriminate grammatical letter strings from non-grammatical letter strings. The success of EPAM IV in simulating human performance strongly indicated that recognition memory based on chunking plays a critical role in implicit learning.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1256-1259
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• 2002

In this paper, we have proposed license plate recognition system for multi-national vehicle license plate using geometric features along with hybrid and seven segment pattern vectors. In the proposed system, we suggested to find horizontal and vertical relation after going through preparation process with inputted real-time license plate image of Korea and Japan, and then to classify license plate with using characteristic and geometric information of license plates. It classifies the extracted license plate images into letters and numbers, such as local name, local number, classification character and license consecutive numbers, and recognize license plate of Korea and Japan by applying hybrid and seven segments pattern vectors to classified letter and number region. License plate extraction step of the proposed system uses width and length information along with relative rate of Korean and Japanese license plate. Moreover, it exactly segmentation by letters with using each letter and number position information within license plate region, and recognizes Korean and Japanese license plates by applying hybrid and seven segment pattern vectors, containing characteristics related to letter size and movement within segmented letter area. As the result of testing the proposed system in real experiment, it recognized regardless of external lighting conditions as well as classifying license plates by nations, Korea and Japan. We have developed a system, recognizing regardless of inputted structural character of vehicle licenses and external environment.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.67-80
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• 2009

In this paper, we described the development of a Korean Manual Alphabet (KMA) learning game with avatar. KMA letters correspond to the vocabulary of Korean Sign Language (KSL) when spelling a word. Each KMA letter corresponds to a letter of the Korean Alphabet (KA) and KA is represented as hand shapes by sign language user. We developed a KMA learning game for a beginner to learn KMA letters from sign language avatar and practice KMA presentation easily. The system composed of sign language teacher avatar GUI popup window based on OpenGL, KMA letter recognition module, KA letter raining game module and USB camera. A user learns a KMA letter with expressing KA syllabic from avatar and inputs a KMA letter to the system using USB camera. We evaluated the efficiency of the developed system through the verification of users.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.4
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• pp.32-38
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• 2000

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-letter. All the letters of each country have a various men 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 Korean, English and Chinese 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 80%.

• 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.

• Korean Journal of Cognitive Science
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• v.1 no.1
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• pp.27-76
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• 1989