• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.220-224
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• 2006

The text to be included in the natural images has many important information in the natural image. Therefore, if we can extract the text in natural images, It can be applied to many important applications. In this paper, we propose a text region extraction method using pattern histogram of character-edge map. We extract the edges with the Canny edge detector and creates 16 kind of edge map from an extracted edges. And then we make a character-edge map of 8 kinds that have a character feature with a combination of an edge map. We extract text region using 8 kinds of character-edge map and 16 kind of edge map. Verification of text candidate region uses analysis of a character-edge map pattern histogram and structural feature of text region. The method to propose experimented with various kind of the natural images. The proposed approach extracted text region from a natural images to have been composed of a complex background, various letters, various text colors effectively.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.64-69
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• 2019

With the advent of the fourth industry, the need for office automation with automatic character recognition capabilities is increasing day by day. Therefore, in this paper, we study a character recognition algorithm that effectively recognizes a new experimental data character by using learning data characters. The proposed algorithm computes the degree of similarity that the structural regions of learning data characters match the corresponding regions of the experimental data character. It has been confirmed that satisfactory results can be obtained by selecting the learning data character with the highest degree of similarity in the matching process as the final recognition result for a given experimental data character.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.819-822
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• 2000

The merit of the stroke extraction algorithm is the ease of the feature abstraction from the skeleton of a character, But, extracting strokes from Korean characters has two major problems that must be dealt with. One is extracting primitive strokes and the other is merging or splitting the strokes using dynamic information of the strokes. In this paper, a method is proposed to extract strokes from an off-line handwritten Korean character. We have developed some stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Korean character accurately and efficiently.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.632-641
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• 2000

Conventional character extraction methods extract character regions using methods such as color reduction, region split and merge and texture analysis from the whole image. Because these methods use many heuristic variables and thresholding values derived from a priori knowledge, it is difficult to generalize them algorithmically. In this paper, we propose a method that can extract character regions using a topographical feature extraction method and a point-line-region extension method. The proposed method can also solve the problems of conventional methods by reducing heuristic variables and generalizing thresholding values. We see that character regions can be extracted by generalized variables and thresolding values without using a priori knowledge of character region. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is over 98%.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.521-526
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• 2005

In this Paper, we propose a keyword spotting system as an alternative to searching system for poor quality Korean document images and compare the Proposed system with an OCR-based document retrieval system. The system is composed of character segmentation, feature extraction for the query keyword, and word-to-word matching. In the character segmentation step, we propose an effective method to remove the connectivity between adjacent characters and a character segmentation method by making the variance of character widths minimum. In the query creation step, feature vector for the query is constructed by a combination of a character model by typeface. In the matching step, word-to-word matching is applied base on a character-to-character matching. We demonstrated that the proposed keyword spotting system is more efficient than the OCR-based one to search a keyword on the Korean document images, especially when the quality of documents is quite poor and point size is small.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1167-1174
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• 2006

Text region detection from a natural scene is useful in many applications such as vehicle license plate recognition. Therefore, in this paper, we propose a text region extraction method using pattern histogram of character-edge maps. We create 16 kinds of edge maps from the extracted edges and then, we create the 8 kinds of edge maps which compound 16 kinds of edge maps, and have a character feature. We extract a candidate of text regions using the 8 kinds of character-edge maps. The verification about candidate of text region used pattern histogram of character-edge maps and structural features of text region. Experimental results show that the proposed method extracts a text regions composed of complex background, various font sizes and font colors effectively.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.16-23
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• 2012

In this paper we discussed a new methodology for Urdu Character Recognition system using Dempster-Shafer theory which can powerfully estimate the similarity ratings between a recognized character and sampling characters in the character database. Recognition of character is done by five probability calculation methods such as (similarity, hamming, linear correlation, cross-correlation, nearest neighbor) with Dempster-Shafer theory of belief functions. The main objective of this paper is to Recognition of Urdu letters and numerals through five similarity and dissimilarity algorithms to find the similarity between the given image and the standard template in the character recognition system. In this paper we develop a method to combine the results of the different distance measurement methods using the Dempster-Shafer theory. This idea enables us to obtain a single precision result. It was observed that the combination of these results ultimately enhanced the success rate.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2549-2551
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• 2003

We propose character recognition system for Gerber files. The Gerber file is the vector-formatted drawing file for PCB manufacturing. To consider the special vector format and rotated characters, we develop segmentation and feature extraction method. The modularized neural network is then applied to the recognition algorithm. Finally, comparative simulation results are presented to verify the usefulness of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.872-877
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• 2012

We proposes the method to detect the Hangul character region from natural image using topological structural feature of Hangul grapheme. First, we transform a natural image to a gray-scale image. Second, feature extraction performed with edge and connected component based method, Edge-based method use a Canny-edge detector and connected component based method applied the local range filtering. Next, if features are not corresponding to the heuristic rule of Hangul character, extracted features filtered out and select candidates of character region. Next, candidates of Hangul character region are merged into one Hangul character using Hangul character merging algorithm. Finally, we detect the final character region by Hangul character class decision algorithm. Experimental result, proposed method could detect a character region effectively in images that contains a complex background and various environments. As a result of the performance evaluation, A proposed method showed advanced results about detection of Hangul character region from mobile image.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.53-56
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• 2002

In this paper, we propose the method of iris lacuna extraction using watershed transform. Lacuna is salient feature of iris. It has three dimensional structure formed by leak of pigmentation and loss of fiber tissues. Lacuna can be used for iris recognition system, and generally used in health diagnosis and character analysis with its shape and position. The main idea of the proposed method is applying the watershed transform to radial gray scale profile of iris image. The result shows that the lacuna can be extracted automatically from eye image.