• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.979-982
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• 2005

This paper is to be pre-processing that decides the text recognizability and quality contained in natural image. Differentiated with the existing studies, In this paper, it suggests the application of partially unified color models, Coiflet Wavelet and text extraction algorithm that uses the closed curve edge features of LoG (laplacian of gaussian)operator. The text image included in natural image such as signboard has the same hue, saturation and value, and there is a certain thickness as for their feature. Each color element is restructured into closed area by LoG operator, the 2nd differential operator. The text area is contracted by Hough Transform, logical AND-OR operator of each color model and Minimum-Distance classifier. This paper targets natural image into which text area is added regardless of the size and resolution of the image, and it is confirmed to have more excellent performance than other algorithms with many restrictions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1135-1143
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• 2021

Recently, text collection using a web crawler for big data analysis has been frequently performed. However, in order to collect only the necessary text from a web page that is complexly composed of numerous tags and texts, there is a cumbersome requirement to specify HTML tags and style attributes that contain the text required for big data analysis in the web crawler. In this paper, we proposed a method of extracting text using the frequency of text appearing in web pages without specifying HTML tags and style attributes. In the proposed method, the text was extracted from the DOM tree of all collected web pages, the frequency of appearance of the text was analyzed, and the main text was extracted by excluding the text with high frequency of appearance. Through this study, the superiority of the proposed method was verified.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1835-1838
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• 2003

Texts in natural images contain significant and detailed informations about the images. Thus, to extract those texts correctly, we suggest a text extraction method using color variance feature. Generally, the texts in images have color variations with the backgrounds. Thus, if we express those variations in 3 dimensional RGB color space, we can emphasize the text regions that can be hard to be captured with a method using intensity variations in the gray-level images. We can even make robust extraction results with the images contaminated by light variations. The color variations are measured by color variance in this paper. First, horizontal and vertical variance images are obtained independently, and we can fine that the text regions have high values of the variances in both directions. Then, the two images are logically ANDed to remove the non-text components with only one directional high variance. We have applied the proposed method to the multiple kinds of the natural images, and we confirmed that the proposed feature can help to find the text regions that can he missed with the following features - intensity variations in the gray-level images and/or color continuity in the color images.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.559-571
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• 2013

This paper has presented a few problems when the 2D video superimposed the overlay text was converted to the 3D stereoscopic video. To resolve the problems, it proposes the scenario which the original video is divided into two parts, one is the video only with overlay text graphic region and the other is the video with holes, and then processed respectively. And this paper focuses on research only to detect and extract the overlay text graphic region, which is a first step among the processes in the proposed scenario. To decide whether the overlay text is included or not within a frame, it is used the corner density map based on the Harris corner detector. Following that, the overlay text region is extracted using the hybrid method of color and motion information of the overlay text region. The experiment shows the results of the overlay text region detection and extraction process in a few genre video sequence.

• 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 Information Technology Services
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• v.19 no.3
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• pp.117-137
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• 2020

The development of information technology leads to changes in various industries. In particular, the health care industry is more influenced so that it is focused on. With the widening of the health care market, the market of smart device based personal health care also draws attention. Since a variety of fitness applications for smartphone based exercise were introduced, more interest has been in the health care industry. But although an amount of use of mobile fitness applications increase, it fails to lead to a sustained use. It is necessary to find and understand what matters for mobile fitness application users. Therefore, this study analyze the reviews of mobile fitness application users, to draw key factors, and thereby to propose detailed strategies for promoting mobile fitness applications. We utilize text mining techniques - LDA topic modeling, term frequency analysis, and keyword extraction - to draw and analyze the issues related to mobile fitness applications. In particular, the key factors drawn by text mining techniques are explained through the concept of user experience. This study is academically meaningful in the point that the key factors of mobile fitness applications are drawn by the user experience based text mining techniques, and practically this study proposes detailed strategies for promoting mobile fitness applications in the health care area.

• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.59-66
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• 2010

Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meet the growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.220-224
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• 2006

The text to be included in the natural images has many important information in the natural image. Therefore, if we can extract the text in natural images, It can be applied to many important applications. In this paper, we propose a text region extraction method using pattern histogram of character-edge map. We extract the edges with the Canny edge detector and creates 16 kind of edge map from an extracted edges. And then we make a character-edge map of 8 kinds that have a character feature with a combination of an edge map. We extract text region using 8 kinds of character-edge map and 16 kind of edge map. Verification of text candidate region uses analysis of a character-edge map pattern histogram and structural feature of text region. The method to propose experimented with various kind of the natural images. The proposed approach extracted text region from a natural images to have been composed of a complex background, various letters, various text colors effectively.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.21-28
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• 2009

Color polarity classification is a process to determine whether the color of text is bright or dark and it is prerequisite task for text extraction. In this paper we propose a color polarity method to extract text region. Based on the observation for the text and background regions, the proposed method uses the ratios of sizes and standard deviations of bright and dark regions. At first, we employ Otsu's method for binarization for gray scale input region. The two largest segments among the bright and the dark regions are selected and the ratio of their sizes is defined as the first measure for color polarity classification. Again, we select the segments that have the smallest standard deviation of the distance from the center among two groups of regions and evaluate the ratio of their standard deviation as the second measure. We use these two ratio features to determine the text color polarity. The proposed method robustly classify color polarity of the text. which has shown by experimental result for the various font and size.