• Journal of Information Processing Systems
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• v.10 no.4
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• pp.503-522
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• 2014

In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes' trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.527-534
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• 2014

Motivated by the fact that English grapheme is realized as 5 different English vowels [ɑ, eɪ, æ, ɔ, ə], the present study aims to investigate how Korean students perceive and produce English grapheme in words. To this end, 31 university students participated in the perception and production tests on 28 English words containing grapheme . Overall, the participants performed better on production than perception both in the pretest and posttest. Also, the accuracy of perception and production was improved in the posttest. While the accuracy rates of [ɑ] and [eɪ] were among highest in the perception test, those of [ɑ] and [ɔ] were in the production test. By contrast, the accuracy of unstressed [ə] was worst both in perception and production. As for the training effect, [eɪ] and [æ] showed significant improvement both in perception and production. In addition, the perception and the production of [ɔ] were improved at the .051-4 significance level. However, the accuracies of [ɑ] improved significantly neither in perception nor in production. In the case of [ə], the production ability was improved significantly whereas the perception ability was not. The prominent error patterns of were the replacement of the target vowels with [æ], [ɑ], or [eɪ]. The influence of orthography was attested through the persistent occurrence of [ɑ]. Finally, pedagogical implications were discussed.

• Korean Journal of Cognitive Science
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• v.26 no.3
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• pp.323-355
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• 2015

This study examined grapheme-to-phoneme regularity effect among late Korean-English bilinguals by using whole word level task (lexical processing) and two meta-phonological tasks(sub-lexical processing): [1] English word naming task(whole word level), [2] rhyme judgement task(rhyme level), and [3] phoneme deletion task(phoneme level). Forty-three late Korean-English bilinguals participated in all three tasks. In these tasks, participants showed better performance in regular word conditions compared to irregular word conditions, demonstrating a clear English regularity effect. Post-hoc correlational analysis revealed strong correlation between word naming task and rhyme judgement task, which is different from the results reported with English monolinguals. The contradicting results might be due to the relevantly low English proficiency level among late Korean-English bilingual speakers. In conclusion, this study suggests that late Korean-English bilinguals make use of L2 grapheme-to-phoneme conversion (GPC) rule when reading L2 English words.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.81-87
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• 1994

This paper describes the recognition of the printed Hanguel(Korean Character) using Neural Network. In this study, Neural network is used in only specific classification. Hanguel is classified globally by using template matching. Neural network is learned using the segmented grapheme. The grapheme of Hanguel is segmented using the structural method. Neural network is constructed, which is corresponded to the kind and the shape of graphemes. Each neural network is multi layer perceptron. The learning algorithm is the modified error back propagation using descending epsilon method. With five test character sets, the recognition rate of 94.95% is obtained.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.175-179
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• 2003

In order to produce a high quality (intelligibility and naturalness) synthesized speech, it is very important to get an accurate grapheme-to-phoneme conversion and prosody model. In this paper, we analyzed Chinese texts using a segmentation, POS tagging and unknown word recognition. We present a grapheme-to-phoneme conversion using a dictionary-based and rule-based method. We constructed a prosody model using a probabilistic method and a decision tree-based error correction method. According to the result from the above analysis, we can successfully select and concatenate exact synthesis unit of syllables from the Chinese Synthesis DB.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.157-160
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• 2017

본 연구에서는 한글 문자 인식을 수행하였다. 한글 인식을 수행하되 고딕 인쇄체 문자를 대상으로 하였고, 자소 단위 인식을 통한 인식을 수행하되 기존 한글 문자 인식 연구에서 사용하는 자음과 모음 단위의 자소가 아닌 연결성분을 이용하여 인식하는 새로운 자소를 이용하였다. 새로운 자소들은 끝점, 2선 모임점, 3선 모임점, 4선 모임점의 특징을 추출하고 특징에 의해 자소를 인식하는 데이터베이스를 구성하여 자소를 인식하게 하였다. 또한 연결 성분을 반영한 새로운 자소로 고딕 인쇄체 문자를 인식하므로 추출된 자소를 6가지로 분류하였고, 6가지 자소에 의해 구성되는 92가지 문자 구조를 제안하고 이에 따른 문자를 데이터베이스를 구축하였고, 자소의 무게 중심을 이용한 분포를 이용하여 제안된 구조를 통하여 데이터베이스를 이용한 문자인식을 수행하였다.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.135-138
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• 2006

In this paper, we introduce a method for extracting grapheme-to-phoneme conversion rules from the transcription of speech synthesis database and a prosody modeling method using the light version of ToBI for a Korean conversational style TTS. We focused on representing the characteristics of the conversational speech style and the experimental results show that our proposed methods are suitable for developing a Korean conversional style TTS.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.87-90
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• 2003

In order to produce high quality synthesized speech, it is very important to get an accurate grapheme-to-phoneme conversion and prosody model from texts using natural language processing. Robust preprocessing for non-Korean characters should also be required. In this paper, we analyzed Korean texts using a morphological analyzer, part-of-speech tagger and syntactic chunker. We present a new grapheme-to-phoneme conversion method, i.e. a dictionary-based and rule-based hybrid method, for unlimited vocabulary Korean TTS. We constructed a prosody model using a probabilistic method and decision tree-based method.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.167-173
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• 2023

Hangul is unique compared to other Asian languages because of its simple letter forms that combine to create syllabic shapes. There are 24 basic letters that can be combined to form 27 additional complex letters. This produces 51 graphemes. Hangul optical character recognition has been a research topic for some time; however, handwritten Hangul recognition continues to be challenging owing to the various writing styles, slants, and cursive-like nature of the handwriting. In this study, a dataset containing thousands of samples of 51 Hangul graphemes was gathered from 110 freshmen university students to create a robust dataset with high variance for training an artificial neural network. The collected dataset included 2200 samples for each consonant grapheme and 1100 samples for each vowel grapheme. The dataset was normalized to the MNIST digits dataset, trained in three neural networks, and the obtained results were compared.

• 한국초전도학회:학술대회논문집
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• 2009.07a
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• pp.79-79
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• 2009