• Journal of Information Processing Systems
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• 제13권5호
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• pp.1397-1409
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• 2017

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

• 정보관리연구
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• 제36권4호
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• pp.51-69
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• 2005

문서분류에 있어서 분류속도의 향상이 중요한 연구과제가 되고 있다. 최근 개발된 자질값투표 기법은 문서자동분류 문제에 대해서 매우 빠른 속도를 가졌지만, 분류정확도는 만족스럽지 못하다. 이 논문에서는 새로운 자질선정 기법인 문서측 자질선정 기법을 제안하고, 이를 자질값투표 기법에 적용해 보았다. 문서측 자질선정은 일반적인 분류자질선정과 달리 학습집단이 아닌 분류대상 문서의 자질 중 일부만을 선택하여 분류에 이용하는 방식이다. 문서측 자질선정을 적용한 실험에서는, 간단하고 빠른 자질값투표 분류기로 SVM 분류기만큼 좋은 성능을 얻을 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• 대한의용생체공학회:의공학회지
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• 제43권6호
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• pp.441-447
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• 2022

In this paper, we propose a pill identification model using engraved text feature and image feature such as shape and color, and compare it with an identification model that does not use engraved text feature to verify the possibility of improving identification performance by improving recognition rate of the engraved text. The data consisted of 100 classes and used 10 images per class. The engraved text feature was acquired through Keras OCR based on deep learning and 1D CNN, and the image feature was acquired through 2D CNN. According to the identification results, the accuracy of the text recognition model was 90%. The accuracy of the comparative model and the proposed model was 91.9% and 97.6%. The accuracy, precision, recall, and F1-score of the proposed model were better than those of the comparative model in terms of statistical significance. As a result, we confirmed that the expansion of the range of feature improved the performance of the identification model.

• Journal of Information Science Theory and Practice
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• 제9권4호
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• pp.15-34
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• 2021

Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.

• Journal of Information Processing Systems
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• 제10권1호
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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.

• 한국지능시스템학회논문지
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• 제25권4호
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• pp.386-391
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• 2015

본 논문에서는 한국어와 중국어의 언어학적인 특징을 고려하여 문서 자동분류 시스템의 성능을 높일 수 있는 최적의 자질어 단위를 제안한다. 언어 종속적 단위인 형태소 자질어와 언어 독립적 단위인 n-gram 자질어 그리고 이들을 조합한 복합 자질어 집합을 대상으로 각 언어의 인터넷 신문기사를 SVM으로 분류하는 실험을 수행하였다. 실험 결과, 한국어 문서분류에서는 bi-gram이 F1-measure 87.07%로 가장 좋은 분류 성능을 보였고, 중국어 문서분류에서는 'uni-gram 명사 동사 형용사 사자성어'의 복합 자질어 집합이 F1-measure 82.79%로 가장 좋은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3725-3748
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• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• 한국문헌정보학회지
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• 제39권2호
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• pp.123-146
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• 2005

이 연구에서는 문서 자동분류에서 분류자질 선정과 가중치 할당을 위해서 일관된 전략을 채택하여 kNN 분류기의 성능을 향상시킬 수 있는 방안을 모색하였다. 문서 자동 분류에서 분류자질 선정 방식과 자질 가중치 할당 방식은 자동분류 알고리즘과 함께 분류성능을 좌우하는 중요한 요소이다. 기존 연구에서는 이 두 방식을 결정할 때 상반된 전략을 사용해왔다. 이 연구에서는 색인파일 저장공간과 실행시간에 따른 분류성능을 기준으로 분류자질 선정 결과를 평가해서 기존 연구와 다른 결과를 얻었다. 상호정보량과 같은 저빈도 자질 선호 기준이나 심지어는 역문헌빈도를 이용해서 분류 자질을 선정하는 것이 kNN 분류기의 분류 효과와 효율 면에서 바람직한 것으로 나타났다. 자질 선정기준으로 저빈도 자질 선호 척도를 자질 선정 및 자질 가중치 할당에 일관되게 이용한 결과 분류성능의 저하 없이 kNN 분류기의 처리 속도를 약 3배에서 5배정도 향상시킬 수 있었다.