• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.81-83
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• 2022

Understanding research trends is necessary to select research topics and explore related works. Most researchers search representative keywords of interesting domains or technologies to understand research trends. However some conferences in artificial intelligence or data mining fields recently publish hundreds to thousands of papers for each year. It makes difficult for researchers to understand research trend of interesting domains. In our paper, we propose an automatic technology keyphrase extraction method to support researcher to understand research trend for each conference or journal. Keyphrase extraction that extracts important terms or phrases from a text, is a fundamental technology for a natural language processing such as summarization or searching, etc. Previous keyphrase extraction technologies based on pretrained language model extract keyphrases from long texts so performances are degraded in short texts like titles of papers. In this paper, we propose a techonolgy keyphrase extraction model that is robust in short text and considers the importance of the word.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.95-101
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• 2019

Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

• 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.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.180-191
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• 2002

We present a new text localization method in images using a multi-layer perceptron(MLP) and a multiple continuously adaptive mean shift (MultiCAMShift) algorithm. An automatically constructed MLP-based texture classifier generates a text probability image for various types of images without an explicit feature extraction. The MultiCAMShift algorithm, which operates on the text probability Image produced by an MLP, can place bounding boxes efficiently without analyzing the texture properties of an entire image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• ETRI Journal
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• v.46 no.1
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• pp.35-47
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• 2024

This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.

• International Journal of Contents
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• v.6 no.2
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• pp.1-5
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• 2010

The rapid growth in communication technology has led to the development of effective ways of sharing ideas and information in the form of speech and images. Understanding this information has become an important research issue and drawn the attention of many researchers. Text in a digital image contains much important information regarding the scene. Detecting and extracting this text is a difficult task and has many challenging issues. The main challenges in extracting text from natural scene images are the variation in the font size, alignment of text, font colors, illumination changes, and reflections in the images. In this paper, we propose a connected component based method to automatically detect the text region in natural images. Since text regions in mages contain mostly repetitions of vertical strokes, we try to find a pattern of closely packed vertical edges. Once the group of edges is found, the neighboring vertical edges are connected to each other. Connected regions whose geometric features lie outside of the valid specifications are considered as outliers and eliminated. The proposed method is more effective than the existing methods for slanted or curved characters. The experimental results are given for the validation of our approach.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.7-19
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• 2019

The aim of this study is to develop a technique for keyword extraction in Korean-Chinese text in the modern period. We considered a Korean morphological analyzer and a particle in classical Chinese as a possible method for this study. We applied our method to the journal "Sisachongbo," employing proper-noun dictionaries and a list of stop words to extract index terms. The results show that our system achieved better performance than a Chinese morphological analyzer in terms of recall and precision. This study is the first research to develop an automatic indexing system in the traditional Korean-Chinese mixed text.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Genomics & Informatics
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• v.19 no.3
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• pp.24.1-24.7
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• 2021

Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)-related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming information regarding this disease. A solution to this problem requires the development of text mining pipelines; the efficiency of which strongly depends on the availability of curated corpora. However, there is a lack of COVID-19-related corpora, even more, if considering other languages besides English. This project's main contribution was the annotation of a multilingual parallel corpus and the generation of a recommendation dataset (EN-PT and EN-ES) regarding relevant entities, their relations, and recommendation, providing this resource to the community to improve the text mining research on COVID-19-related literature. This work was developed during the 7th Biomedical Linked Annotation Hackathon (BLAH7).