• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.3
• /
• pp.215-220
• /
• 2010

In this paper, we propose a new texture-based page segmentation and classification method in which table region, background region, image region and text region in a given document image are automatically identified. The proposed method for document images consists of two stages, document segmentation and contents classification. In the first stage, we segment the document image, and then, we classify contents of document in the second stage. The proposed classification method is based on a texture analysis. Each contents in the document are considered as regions with different textures. Thus the problem of classification contents of document can be posed as a texture segmentation and analysis problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. Our method does not assume any a priori knowledge about content or language of the document. As we can see experiment results, our method gives good performance in document segmentation and contents classification. The proposed system is expected to apply such as multimedia data searching, real-time image processing.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.3
• /
• pp.410-422
• /
• 1997

In this paper, we descried a bottom-up document structure analysis method in bilingual Korean-English document. We proposed a character segmentation method based on the layout information of connected component of each character. In many researches, a document has been analyzed into text blocks and graphics. We analyzed a document into four parts: text, table, graphic, and separator. A text is recursively subdivided into text blocks, text lines, words, and characters. To extract the character in bilingual text, we proposed a new method of word of word separation of Korean or English. Futhermore, we used a character merging and segmentation method in accordance with the properties of Hangul on the Korean word blocks. Experimental results on the various documents show that the proposed method is very effectively operated on the document structure analysis and the character segmentation.

• International Journal of Contents
• /
• v.10 no.3
• /
• pp.35-40
• /
• 2014

Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-line-segmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection (AHTC) on the document. Then, the watershed method segments the line region using the baseline-detection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.198-201
• /
• 2000

We propose a method for analyzing the document structure. This method consists of two processes, segmentation and classification. The segmentation first divides a low resolution image, and then finely splits the original document image using projection profiles. The classification deterimines each segmented region as text, line, table or image. An experiment with 238 documents images shows that the segmentation accuracy is 99.1% and the classification accuracy is 97.3%.

• Journal of the Korea Society of Computer and Information
• /
• v.6 no.1
• /
• pp.82-89
• /
• 2001

It is a document segmentation to set a boundary in the documents by the contents. It is essential for the accurate and efficient information search. In this paper we want to make an automatic document segmentation system with the method of probability analysis which uses the mutual information between the words. Proposed system can move the boundary of window and compute the similarity or the two window. In this system the more words are shared and the more important the words are, the higher the cohesive force of the two window systems goes. The result of experience with the document segmentation is that despite the differences of block unit the division point at which we expected to divide was normally divided.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.3
• /
• pp.209-214
• /
• 2010

This paper proposes an algorithm of segmentation and title detection for document image. The automated title detection method that we have developed is composed of two phases, segmentation and title area detection. In the first phase, we extract and segment the document image. To perform this operation, the binary map is segmented by combination of morphological operation and CCA(connected component algorithm). The first phase provides segmented regions that would be detected as title area for the second stage. Candidate title areas are detected using geometric information, then we can extract the title region that is performed by removing non-title regions. After classification step that removes non-text regions, projection is performed to detect a title region. From the fact that usually the largest font is used for the title in the document, horizontal projection is performed within text areas. In this paper, we proposed a method of segmentation and title detection for various forms of document images using geometric features and projection profile analysis. The proposed system is expected to have various applications, such as document title recognition, multimedia data searching, real-time image processing and so on.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.10
• /
• pp.120-129
• /
• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1391-1394
• /
• 1997

This paper proposes a new image processing algorithm to recognize korean documents. It take out the region of text area form input image, then it makes esgmentation of lines, words and characters in the text. A precision segmentation is very important to recognize the input document. The input image has 8-bit gray scaled resolution. Not only the histogram but also brightness dispersion graph are used for segmentation. The result shows a higher accuracy of document recognition.

• International Journal of Contents
• /
• v.11 no.2
• /
• pp.1-8
• /
• 2015

In this paper, we describe a novel method for document layout analysis that is based on a Fuzzy Energy Matrix (FEM). A FEM is a two-dimensional matrix that contains the likelihood of text and non-text and is generated through the use of Fuzzy theory. The key idea is to define an Energy map for the document to categorize text and non-text. The proposed mechanism is designed for execution with a low-resolution document image, and hence our method has a fast processing speed. The proposed method has been tested on public ICDAR 2009 datasets to conduct a comparison against other state-of-the-art methods, and it was also tested with Korean documents. The results of the experiment indicate that this scheme achieves superior segmentation accuracy, in terms of both precision and recall, and also requires less time for computation than other state-of-the-art document image analysis methods.

• The KIPS Transactions:PartB
• /
• v.12B no.6 s.102
• /
• pp.697-702
• /
• 2005

The focused crawler is a topic-driven document-collecting crawler that was suggested as a promising alternative of maintaining up-to-date web document Indices in search engines. A major problem inherent in previous focused crawlers is the liability of missing highly relevant documents that are linked from off-topic documents. This problem mainly originated from the lack of consideration of structural information in a document. Traditional weighting method such as TFIDF employed in document classification can lead to this problem. In order to improve the performance of focused crawlers, this paper proposes a scheme of locality-based document segmentation to determine the relevance of a document to a specific topic. We segment a document into a set of sub-documents using contextual features around the hyperlinks. This information is used to determine whether the crawler would fetch the documents that are linked from hyperlinks in an off-topic document.