• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.81-83
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• 2022

연구 동향을 파악하는 것은 연구 수행 시 필수적인 요소이다. 대부분의 연구자들은 관심분야의 학술대회 및 저널을 대표하는 기술 핵심구나 관심 분야를 검색함으로써 연구 동향을 파악한다. 하지만, 최근 인공지능과 같은 특정 분야의 경우 한 개의 학술대회에 한 해당 수백~수천 개의 논문이 출간되기 때문에 전체 분야의 경향성을 파악하는 데 어려움이 존재한다. 본 논문에서는 학술대회 또는 저널 제목을 활용하여 기술 핵심구를 자동으로 추출함으로써 연도별 학술대회 및 저널의 연구 동향 파악을 지원하고자 한다. 핵심구 추출은 문장 또는 문서를 대표하는 주요 구문을 추출하는 작업으로서 검색, 요약, 내용 파악 등을 위해 근간이 되는 기술이다. 기존 사전학습 언어모델 기반의 핵심구 추출 모델은 문서 단위의 긴 텍스트를 기준으로 모델링 하였기 때문에 제목 단위의 짧은 텍스트에서는 성능이 낮아진다는 단점이 존재한다. 본 논문에서는 짧은 텍스트에 강인하면서 단어 자체의 중요도를 고려한 학술대회 및 저널의 기술 핵심구 추출 모델을 제안하고자 한다.

• International journal of advanced smart convergence
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• 제8권3호
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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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• 제22권7호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권3호
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• pp.180-191
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• 2002

본 논문은 MLP와 MultiCAMShift 알고리즘을 이용한 텍스춰 기반의 영상 내 문자 추출 방법을 제안한다. MLP를 이용한 텍스춰 분석기는 별도의 특징값 추출 단계 없이 다양한 환경의 입력 영상에 대해 효과적으로 문자 확률 영상을 생성하며, 문자 확률 영상 상에서 수행되는 MultiCAMShift 알고리즘은 국소 탐색만으로 효율적으로 문자 영역을 추출할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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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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• 제46권1호
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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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• 제6권2호
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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.

• 정보관리학회지
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• 제36권4호
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• pp.7-19
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• 2019

본 연구는 국한문 혼용 텍스트를 대상으로 한글 형태소 분석 기법과 한문 어조사를 반영한 색인어 추출기법을 제안하였다. 국한문 혼용체로 작성된 『시사총보』 논설을 대상으로 해당 시기에 사용된 고유명사 및 한자어 사전을 보완하였으며 한자어 불용어 리스트를 고려하여 색인어를 추출하였다. 본 연구에서 제안한 국한문 색인 시스템은 수작업 색인 결과를 기준으로, 중국어형태소 분석기에 비해 재현율과 정확률 측면에서 상대적으로 높은 성능을 보였으며, 어문법이 확립되지 않은 근현대 시기의 국한문 혼용체를 대상으로 한 첫 번째 색인어 추출기법을 제안하였다는 데에서 연구의 차별점이 있다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권12호
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• pp.1009-1015
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• 2009

필적 감정은 개인의 필적 개성을 이용하여 임의의 두 필기 문장 또는 텍스트가 동일인에 의해 작성되었는지를 판별하는 기술이다. 본 논문은 패턴 인식 기술을 사용하여 효과적으로 필적을 분석하고 판별하는 오프-라인 환경에서의 검증 방법을 제안한다. 본 논문에서 연구된 방법의 핵심 절차는 문자 영역 추출, 문서의 구조적 특징을 반영하는 특징의 추출, DTW(Dynamic Time Warping) 알고리즘과 주성분 분석을 이용한 특징 분석이다. 실험 결과는 제안하는 방법의 우수한 성능을 보여준다.

• Genomics & Informatics
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• 제19권3호
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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).