• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.82-92
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• 1997

In this paper, we propose post-rpocessing method that corrects a misrecognized character by generated a characater recognizer using morphological analyzer and spelling corrector. The proposed post-processing consists of sthree phases : First, our method pass through morhological analyzer which only outputted necessary information for spelling correcting, doesn't analyze a bundle of phrases, and detects the location of misrecognized character. Second, tagging the generated candidate character using the information of character substitution table and grapheme substitution/separating table. Then we retry analysis after the misrecognition character has been substituted. Finally we select table, we investigate misrecognized charcters in CORPUS. Reliability analysis used to frequency of randomly selected about 100,000 words in CORPUS. A korean character recognizer demonstrates 93% correction rate without a post-processing. The entire recognition rate of our system with a post-processing exceeds 97% correction rate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.571-574
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• 2003

This research is aimed to automate the generating process of the database from paper based table forms like this work. The registration table has so complicate table structures, ana in this research we used the registration tables as an example of general table structure understanding. We propose a table structure understanding system for some table types, and it has some steps. The first step is that the document images on paper are read from the image scanner. The second step is that a document image segments into some tables. In the third step, the character strings is extracted using image processing technology and the property of the character strings is determined. And the structured database is generated automatically. The proposed system consists of two systems. "Master document generation system" is used for the table form definition, and it doesn′t include the handwritten characters. "Structure analysis system for complete d table" is used for the written form, and it analyzes the table form filled in the handwritten character. We implemented the system using MS Visual C＋＋ on Windows, and it can get the correct extraction rate 98％ among 51 registration tables written by the different students.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.651-658
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• 2021

In this paper, we propose methods for character recognition and classification for table items through deep learning. First, table areas are detected in a document image through CNN. After that, table areas are separated by separators such as vertical lines. The text in document is recognized through a neural network combined with CNN and RNN. To correct errors in the character recognition, multiple candidates for the recognized result are provided for a sentence which has low recognition accuracy.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.695-702
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• 2022

In this paper, we propose the method of a table structure recognition in various table types for document processing automation. A table with items surrounded by ruled lines are analyzed by detecting horizontal and vertical lines for recognizing the table structure. In case of a table with items separated by spaces, the table structure are recognized by analyzing the arrangement of row items. After recognizing the table structure, the areas of the table items are input into OCR engine and the character recognition result output to a text file in a structured format such as CSV or JSON. In simulation results, the average accuracy of table item recognition is about 94%.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1363-1366
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• 2003

This paper introduces computerized version of Hangout(Korean Language) Standard Pronunciation Rule that can be used in Korean processing systems such as Korean voice synthesis system and Korean voice recognition system. For this purpose, we build Petri net models for each items of the Standard Pronunciation Rule, and then integrate them into the vocal sound conversion table. The reversion of Hangul Standard Pronunciation Rule regulates the way of matching vocal sounds into grammatically correct written characters. This paper presents not only the vocal sound conversion table but also character conversion table obtained by reversely converting the vocal sound conversion table. Making use of these tables, we have implemented a Hangeul character into a vocal sound system and a Korean vocal sound into character conversion system, and tested them with various data sets reflecting all the items of the Standard Pronunciation Rule to verify the soundness and completeness of our tables. The test results shows that the tables improves the process speed in addition to the soundness and completeness.

• Language and Information
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• v.7 no.2
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• pp.81-101
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• 2003

This paper introduces a computerized version of the Korean Standard Pronunciation Rules that can be used in speech engineering systems such as Korean speech synthesis and recognition systems. For this purpose, we build Petri net models for each item of the Standard Pronunciation Rules, and then integrate them into the sound conversion table. The reversion of the Korean Standard Pronunciation Rules regulates the way of matching sounds into grammatically correct written characters. This paper presents not only the sound conversion table but also the character conversion table obtained by reversely converting the sound conversion table. Malting use of these tables, we have implemented a Korean character into a sound system and a Korean sound into the character conversion system, and tested them with various data sets reflecting all the items of the Standard Pronunciation Rules to verify the soundness and completeness of our tables. The test results show that the tables improve the process speed in addition to the soundness and completeness.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1147-1159
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• 1996

In this paper. we develop an efficient algorithm which vectorize the table input for mixed document recognition system. It is necessary to separate character and line for recognizing the character in the table. For recognizing table, we have to recognize the character which is blocked by table line and develop the efficient rectorization method for table line. For vectorizing table, we develop several methods. The first method is to extract table line part using 8-dircction chaincodes. The second method is to extract horizontal and vertical lines using histogram of lines. The third one is to extract diagonal lines of table by using the cross points of horizontal and verticallines. Finally we also develop the table vectorization method which finds the regularity characteristics of horizontal and vertical lines composing table, In the paper, we sugest a regularity method for efficient table vectorization.

• Proceedings of the Korea Society of Design Studies Conference
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• 1996.04a
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• pp.3-4
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• 1996

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.945-956
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• 2013

The character industry is a high value-added industry, and is one of the strategic industries to be fostered. However, the character industry is struggling due to the lack of national consensus on the importance and value of the character industry. Therefore, in order to resolve this issue, the study used the character Input-Output Table of year 2009 of korea to analyze how much the character industry(Toys and games, Models and decorations) contributes to the national economy by measuring economic spreading effects of character industry on national economy. The results shows that character industry shows that production inducement coefficient is column 1.602, row 1.007, index of the sensitivity of dispersion is 0.543, Index of the power of dispersion is 0.864, value-added coefficient is 0.620, income inducement coefficient is 0.334, tax inducement coefficient is 0.066, employment inducement coefficient is 0.008.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.170-177
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• 2016

Character encoding is a method for changing a document to a binary document file using the code table for storage in a computer. When people decode a binary document file in a computer to be read, they must know the code table applied to the file at the encoding stage in order to get the original document. Identifying the code table used for encoding the file is thus an essential part of decoding. In this paper, we propose a method for detecting the character code of the given binary document file automatically. The method uses many techniques to increase the detection rate, such as a character code range detection, escape character detection, character code characteristic detection, and commonly used word detection. The commonly used word detection method uses multiple word database, which means this method can achieve a much higher detection rate for multi-language files as compared with other methods. If the proportion of language is 20% less than in the document, the conventional method has about 50% encoding recognition. In the case of the proposed method, regardless of the proportion of language, there is up to 96% encoding recognition.