• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1140-1152
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• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.693-694
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• 2023

본 논문에서는 FastText 알고리즘을 기반으로 한 사용자 지정 키워드 기반 동영상 요약 시스템을 제안한다. 사용자가 키워드를 입력하면 시스템은 해당 키워드와 관련된 단어들을 FastText를 통해 추출하며, 이를 STT (Speech-to-Text)로 변환된 동영상에서 타임 스탬프 기반으로 인식한다. 인식된 키워드와 관련된 내용은 클립 형식으로 요약되어 사용자에게 제공된다. 본 연구의 목적은 숏폼 콘텐츠 환경에서 효과적인 콘텐츠 추출 및 제공을 통해 사용자 경험과 정보 제공의 효율성을 향상시키기 위함이다. 제안된 시스템은 사용자 지정 키워드에 맞춰 다양한 동영상 플랫폼에서 효율적인 영상 요약을 제공함으로써 온라인 동영상 환경에서 큰 혁신을 이끌어낼 것으로 기대된다.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.280-289
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• 2016

User authentication is an important issue on computer network systems. Most of the current computer network systems use the ID-password string match as the primary user authentication method. However, in password-based authentication, whoever acquires the password of a valid user can access the system without any restrictions. In this paper, we present a keystroke dynamics-based user authentication to resolve limitations of the password-based authentication. Since most previous studies employed a fixed-length text as an input data, we aims at enhancing the authentication performance by combining four different variable creation methods from a variable-length free text as an input data. As authentication algorithms, four one-class classifiers are employed. We verify the proposed approach through an experiment based on actual keystroke data collected from 100 participants who provided more than 17,000 keystrokes for both Korean and English. The experimental results show that our proposed method significantly improve the authentication performance compared to the existing approaches.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.147-155
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• 2016

According to rapid development of technology, web text is growing explosively and attracting many fields as substitution for survey. The user of Facebook is reaching up to 113 million people per month, Twitter is used in various institution or company as a behavioral analysis tool. However, many research has focused on meaning of the text itself. And there is a lack of study for text's creation subject. Therefore, this research consists of sex/age text classification with by using 20,187 Facebook users' posts that reveal the sex and age of the writer. This research utilized Convolution Neural Networks, a type of deep learning algorithms which came into the spotlight as a recent image classifier in web text analyzing. The following result assured with 92% of accuracy for possibility as a text classifier. Also, this research was minimizing the Korean morpheme analysis and it was conducted using a Korean web text to Authorship Attribution. Based on these feature, this study can develop users' multiple capacity such as web text management information resource for worker, non-grammatical analyzing system for researchers. Thus, this study proposes a new method for web text analysis.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.27-39
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• 2002

A locating text technique capable of locating and extracting text blocks in various Web images is presented here. Until now this area of work has been ignored by researchers even if this sort of text may be meaningful for internet users. The algorithms associated with the technique work without prior knowledge of the text orientation, size or font. In the work presented in this research, our text extraction algorithm utilizes useful edge detection followed by histogram analysis on the genuine characteristics of letters defined by text clustering region, to properly perform extraction of the text region that does not depend on font styles and sizes. By a number of experiments we have showed impressively acceptable results.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.57-67
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• 2021

Recently, multi-modal deep learning techniques that combine heterogeneous data for deep learning analysis have been utilized a lot. In particular, studies on the synthesis of Text to Image that automatically generate images from text are being actively conducted. Deep learning for image synthesis requires a vast amount of data consisting of pairs of images and text describing the image. Therefore, various data augmentation techniques have been devised to generate a large amount of data from small data. A number of text augmentation techniques based on synonym replacement have been proposed so far. However, these techniques have a common limitation in that there is a possibility of generating a incorrect text from the content of an image when replacing the synonym for a noun word. In this study, we propose a text augmentation method to replace words using word hierarchy information for noun words. Additionally, we performed experiments using MSCOCO data in order to evaluate the performance of the proposed methodology.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.93-105
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• 2022

This study investigated the aspects of how three preservice elementary school teachers conducted the data-text transforming discourses in their science simulation teaching and how their epistemological conversations worked for learners' construction of scientific knowledge. Three preservice teachers, who had presented simulation teaching on the seasonal change of constellations, participated in the study. The results revealed that one preservice teacher, who had implemented the transforming discourses of data-to-evidence and model-to-explanation, appeared to facilitate learners' knowledge construction. The other two preservice teachers had difficulty helping learners construct science knowledge due to their lack of transforming discourses. What we should consider for improving preservice elementary school teachers' teaching competencies was discussed based on a detailed comparison of three cases of preservice teachers' data-text transforming.

• Smart Media Journal
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• v.12 no.7
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• pp.76-83
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• 2023

This study proposes a method to assist webtoon artists in the process of webtoon creation by utilizing a pretrained Text-to-Image model to generate webtoon images from text. The proposed approach involves fine-tuning a pretrained Stable Diffusion model using a webtoon dataset transformed into the desired webtoon style. The fine-tuning process, using LoRA technique, completes in a quick training time of approximately 4.5 hours with 30,000 steps. The generated images exhibit the representation of shapes and backgrounds based on the input text, resulting in the creation of webtoon-like images. Furthermore, the quantitative evaluation using the Inception score shows that the proposed method outperforms DCGAN-based Text-to-Image models. If webtoon artists adopt the proposed Text-to-Image model for webtoon creation, it is expected to significantly reduce the time required for the creative process.