• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Journal of Information Science Theory and Practice
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• v.9 no.4
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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.

• 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 Information Processing Systems
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• v.14 no.6
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• Journal of the Korean Society for information Management
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• v.4 no.1
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• pp.24-46
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• 1987

The recent growth of full-text database operations has brought new opportunities for subject access. The fundamental problem of subject access in the online environment is the indexing language and technology. The purpose of this paper is to identify the characteristics and capabilities of full-text retrieval as compared to traditional bibliographic retrieval. Retrieval performance of indexing languages, full-text systems features achieved so far, and the new role of a controlled vocabulary, are examined. This paper also includes a review of the research on full-text retrieval performance.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.262-268
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• 2008

Recently, as the demand for multimedia services based on the wireless technologies and mobile devices increases, Full-touch screen mobile devices adopting touch screen keyboards are emerging to cope with the limited display size and take advantage of the flexibility in the design of user interfaces. However, the text entry task, which is one of the main features of the mobile devices, decreases the competitive advantages of the touch screen keyboards over the physical keyboards or keypads due to the lack of physical feedbacks and the frequent occurrence of mistyping. This study aims to introduce a novel text entry method named Fuzzy Touch and compare this method with the conventional text entry method on a touch screen keyboard in terms of the performance (time, number of touch) and the subjective ratings (ease of use, overall preference).

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.415-420
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• 2002

This paper presents an efficient text categorization algorithm that generates high quality bigrams by using the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to a Naive Bayes classifier. The experimental results suggest that the bigrams, while small in number, can substantially contribute to improving text categorization. Upon close examination of the results, we conclude that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly.

• International Journal of Contents
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• v.8 no.4
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• pp.95-102
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• 2012

In this paper, we present a technique to extract the title areas from book cover images. A typical book cover image may contain text, pictures, diagrams as well as complex and irregular background. In addition, the high variability of character features such as thickness, font, position, background and tilt of the text also makes the text extraction task more complicated. Therefore, we propose a two steps efficient method that uses Histogram of Oriented Gradients and color information to find the title areas. Firstly, text localization is carried out to find the title candidates. Finally, refinement process is performed to find the sufficient components of title areas. To obtain the best result, we also use other constraints about the size, ratio between the length and width of the title. We achieve encouraging results of extracted title regions from book cover images which prove the advantages and efficiency of the proposed method.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.