• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.73-82
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• 2009

This paper proposes a variance recovery method for character strokes that can be missed in applying the previously proposed color variance approach in text detection of natural scene images. The previous method has a shortcoming of missing the color variance due to the fixed length of horizontal and vertical windows of variance detection when the character strokes are thick or long. Thus, this paper proposes a variance recovery method by using geometric information of bounding boxes of connected components and heuristic knowledge. We have tested the proposed method using various kinds of document-style and natural scene images such as billboards, signboards, etc captured by digital cameras and mobile-phone cameras. And we showed the improved text detection accuracy even in the images of containing large characters.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• Smart Media Journal
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• v.1 no.4
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.59-66
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• 2008

This paper describes a novel method for identifying the main topic and detecting topic changes in a text-based dialogue as in Instant Messaging (IM). Compared to other forms of text, dialogues are uniquely characterized with the short length of text with small number of words, two or more participants, and existence of a history that affects the current utterance. Noting the characteristics, our method detects the main topic of a dialogue by considering the keywords not only the utterances of the user but also the dialogue system's responses. Dialogue histories are also considered in the detection process to increase accuracy. For topic change detection, the similarity between the former utterance's topic and the current utterance's topic is calculated. If the similarity is smaller than a certain threshold, our system judges that the topic has been changed from the current utterance. We obtained 88.2% and 87.4% accuracy in topic detection and topic change detection, respectively.

• Journal of Information Technology Applications and Management
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• v.28 no.5
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.882-892
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• 2017

Locally Likely Arrangement Hashing (LLAH) is a method that describes image features based on the geometry between their neighbors. Thus, it has been preferred to implement augmented reality on poorly-textured objects such as text documents. However, LLAH strongly requires that image features be detected with high repeatability and located at a distance from one another. To fulfill the requirement for text document, this paper proposes a method that facilitates the word detection in infrared (IR) range by adjusting the IR-reflectance of words. Specifically, the words are printed out with two different black inks: one is using the K(carbon black) ink only, the other is mixing the C(cyan), M(magenta), Y(yellow) inks. Since only the words printed out with the K ink is visible in IR range, a part of words are selected in advance to be used as features and printed out the K ink. The selected words can be robustly detected with high repeatability in IR range and this enables to implement augmented reality on text documents with high fidelity. The validity of the proposed method was verified through experiments.

• International Journal of Contents
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• v.9 no.2
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• pp.8-13
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• 2013

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2866-2879
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.