• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.5-21
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• 2006

In recent years, theories of image and sound analysis have been proposed to work with text retrieval systems and have progressed quickly with the rapid progress in data processing speeds. This study proposes a common representation format for multimedia documents (CRFMD) composed of both images and text to form a single data structure. It also shows that image classification of a given test set is dramatically improved when text features are encoded together with image features. CRFMD might be applicable to other areas of multimedia document retrieval and processing, such as medical image retrieval, World Wide Web searching, and museum collection retrieval.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.218-225
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• 2007

This paper presents a technique to detect a document skew that often occurs during document scanning. To correct a skewed document is essential for automatic processing system including character segmentation, character recognition and so on. The proposed algorithm can detect a skew angle exactly by searching characters baselines that have slant information of the document within a candidated area. To reduce processing time, we resized the image small and then established a ROI (region of interest) by morphology operations and connected components analysis. We compared our method with the existing method based on morphology operations and proved correctness and efficiency of the proposed algorithm through experiments and analysis with various kind of document images.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.369-378
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• 2005

Document image is segmented and classified into text, picture, or table by a document layout analysis, and the words in table regions are significant for keyword spotting because they are more meaningful than the words in other regions. This paper proposes a method to extract words from table regions in document images. As word extraction from table regions is practically regarded extracting words from cell regions composing the table, it is necessary to extract the cell correctly. In the cell extraction module, table frame is extracted first by analyzing connected components, and then the intersection points are extracted from the table frame. We modify the false intersections using the correlation between the neighboring intersections, and extract the cells using the information of intersections. Text regions in the individual cells are located by using the connected components information that was obtained during the cell extraction module, and they are segmented into text lines by using projection profiles. Finally we divide the segmented lines into words using gap clustering and special symbol detection. The experiment performed on In table images that are extracted from Korean documents, and shows $99.16\%$ accuracy of word extraction.

• International Journal of Contents
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• v.11 no.2
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.377-384
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• 2009

A new algorithm in order to classify various contents in the image documents, such as text, figure, graph, table, etc. is proposed in this paper by classifying contents using texture-based PCA, and by segmenting document images using local entropy-based histogram. Local entropy and histogram made the binarization of image document not only robust to various transformation and noise, but also easy and less time-consuming. And texture-based PCA algorithm for each segmented region was taken notice of each content in the image documents having different texture information. Through this, it was not necessary to establish any pre-defined structural information, and advantages were found from the fact of fast and efficient classification. The result demonstrated that the proposed method had shown better performances of segmentation and classification for various images, and is also found superior to previous methods by its efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.805-813
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• 2002

Document image should be classified into detailed regions as text, picture, table and etc through the geometric layout analysis if paper documents can be converted automatically into electronic documents. However, complexity of the document layout and variety of the size and density of a picture are the reason to make it difficult to analyze the geometric layout of the document images. In this paper, we propose the method which have a better performance of the region segmentation and classifications, and the line extraction in the table region than the commercial softwares and previous methods. The proposed method can segment the document into detailed regions by using connected components method even if its layout is complex. This method also classifies texts and pictures by using separable median filter even. Though their size and density are diverse, In addition, this method extracts the lines from the table adapting one dimensional median filter to the each horizontal and vertical direction, even though lines are deformed or texts attached to them.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.556-563
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• 2006

We should extract lines and data which consist of the table in order to classify the table region and analyze its structure in document image. But it is difficult to extract lines and data exactly because the lines are cut and their lengths are changed, or characters or noises are merged to the table lines. These problems result from the error of image input device or image reduction. In this paper, we propose the better method of extracting lines and data for table region classification and structure analysis than the previous ones including commercial softwares. The prposed method extracts horizontal and vertical lines which consist of the table by the use of one dimensional median filter. This filter not only eliminates the noises which attach to the line and the lines which are orthogonal to the filtering direction, but also connects the cut line of which the gap is shorter than the length of the filter tap in the process of extracting lines to the filtering direction. Furthermore, texts attached to the line are separated in the process of extracting vertical lines. This is an example of ABSTRACT format.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.795-813
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• 2001

Sophisticated geometric structure analysis must be preceded to create electronic document from logical components extracted from document image. this paper presents a knowledge-based method for sophisticated geometric structure analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result of segmentation process does not have a one-to-one matching with composite layout components. Therefore, the proposed method identifies non-text objects such as image, drawing and table, as well as text objects such as text line and equation by splitting or grouping segmented regions into composite layout components. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence show that the proposed method has performed geometrical structure analysis successfully on more than 99% of the test images, resulting in sophisticated performance compared with previous works.

• Journal of Digital Contents Society
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• v.15 no.2
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• pp.209-217
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• 2014

It is very important to classify a lot of table-form documents into the same type of classes or to extract information filled in the template automatically. For these, it is necessary to accurately analyze table-form structure. This paper proposes an algorithm to extract corner points based on line edge segments and to classify the type of junction from table-form images. The algorithm preprocesses image through binarization, skew correction, deletion of isolated small area of black color because that they are probably generated by noises.. And then, it processes detections of edge block, line edges from a edge block, corner points. The extracted corner points are classified as 9 types of junction based on the combination of horizontal/vertical line edge segments in a block. The proposed method is applied to the several unconstraint document images such as tax form, transaction receipt, ordinary document containing tables, etc. The experimental results show that the performance of point detection is over 99%. Considering that almost corner points make a correspondence pair in the table, the information of type of corner and width of line may be useful to analyse the structure of table-form document.

• Journal of Digital Contents Society
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• v.4 no.2
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• pp.211-221
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• 2003

The objective of this paper is performance analysis of the digital document image decimation algorithm which generates a value of decimated element by an average of a target pixel value and a value of neighbor intelligible element to adaptively reflect the merits of ZOD method and FOD method on the decimated image. First, a target pixel located at the center of sliding window is selected, then the gradient amplitudes of its right neighbor pixel and its lower neighbor pixel are calculated using first order derivative operator respectively. Secondly, each gradient amplitude is divided by the summation result of two gradient amplitudes to generate each local intelligible weight. Next, a value of neighbor intelligible element is obtained by adding a value of the right neighbor pixel times its local intelligible weight to a value of the lower neighbor pixel times its intelligible weight. The decimated image can be acquired by applying the process repetitively to all pixels in input image which generates the value of decimated element by calculating the average of the target pixel value and the value of neighbor intelligible element. In this paper, the performance comparison of proposed method and conventional methods in terms of subjective performance and hardware complexity is analyzed and the preferable approach for developing the decimation algorithm of the digital document image on the basis of this analysis result has been reviewed.