• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.131-136
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• 2007

This paper proposes a new approach to eliminate the reflectance component for the detection of text in color images. Color images, printed by color printing technology, normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. We were able to go ahead through the ability to extract by reducing the blur phenomenon of text by light because reflection component by an illumination change and removed it and extracted text. The experimental results have shown a superior performance even when an image has a complex background. Text detection and recognition performance is influenced by changing the illumination condition. Our method is robust to the images with different illumination conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• Convergence Security Journal
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• v.22 no.5
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• pp.11-20
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• 2022

As the Internet and web technology develop around mobile devices, image data contains various types of sensitive information such as people, text, and space. In addition to these characteristics, as the use of SNS increases, the amount of damage caused by exposure and abuse of personal information online is increasing. However, research on de-identification technology based on multi-type object detection for personal information protection is insufficient. Therefore, this paper proposes an artificial intelligence model that detects and de-identifies multiple types of objects using existing single-type object detection models in parallel. Through cutmix, an image in which person and text objects exist together are created and composed of training data, and detection and de-identification of objects with different characteristics of person and text was performed. The proposed model achieves a precision of 0.724 and mAP@.5 of 0.745 when two objects are present at the same time. In addition, after de-identification, mAP@.5 was 0.224 for all objects, showing a decrease of 0.4 or more.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.55-68
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• 2014

Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.153-161
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• 2018

In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.55-67
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• 2014

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.61-68
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• 2018

In this paper, we propose a method of character detection by analyzing image enhancement and character type to detect characters in natural images that can be acquired in everyday life. The proposed method emphasizes the boundaries of the object part using the unsharp mask in order to improve the detection rate of the area to be recognized as a character in a natural image. By using the boundary of the enhanced object, the character candidate region of the image is detected using Maximal Stable Extermal Regions (MSER). In order to detect the region to be judged as a real character in the detected character candidate region, the shape of each region is analyzed and the non-character region other than the region having the character characteristic is removed to increase the detection rate of the actual character region. In order to compare the objective test of this paper, we compare the detection rate and the accuracy of the character region with the existing methods. Experimental results show that the proposed method improves the detection rate and accuracy of the character region over the existing character detection method.