• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• Proceedings of the KIEE Conference
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• 1998.07e
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• pp.1846-1849
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• 1998

An optical magnetic filed sensor, YIG (Yittrium Iron Garnet), response rapidly, to the power-line currents to be measured. In response to Faraday effect, it shows wide-and-linear measurable range. This paper reports the development and implementation of an OOCR (Optoelectric OverCurrent Relay) using this sensor. This relay is free from environmental electro-magnetic noises and has excellent insulation characteristics inherently. By using this OOCR, we can reduce insulation cost of protective relaying systems and its maintenance cost, and can achieve high reliability of the system.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.383-387
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• 2012

Development of automated document management system specified for scanned invoice images suffers from rigorous accuracy requirements for extraction of monetary data, which necessiate automatic validation on the extracted values for a generative invoice table model. Use of certain internal constraints such as "amount = unit price times quantity" is typical implementation. In this paper, we propose a noble invoice information extraction model with improved auto-validation method by utilizing table header detection and column classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.299-306
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• 2020

Image recognition using neural networks is widely used in various fields. In this study, we investigated licensed / unlicensed vehicle recognition systems necessary for vehicle number recognition and control when entering and exiting a specific area. This system is equipped with the function of recognizing the image, so it checks all the information on the vehicle number and adds the function to accurately recognize the vehicle number plate. In addition, it is possible to check the vehicle number more quickly using a neural network.

• Journal of the Semiconductor & Display Technology
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• v.7 no.1
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• pp.41-45
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• 2008

We studied the electrical characteristics of low-k SiOCR interlayer dielectric(ILD) films fabricated by plasma enhanced chemical vapor deposition (PECVD). The precursor bis-trimethylsilylmethane (BTMSM) was introduced into the reaction chamber with the various flow rates. The absorption intensities of Si-O-$CH_x$, bonding group and Si-$CH_x$, bonding group changed synchronously for the variation of precursor flow rate, but the intensity of Si-O-Si(C) responded asynchronously with the $CH_x$, combined bonds. The SiOCH films revealed ultra low dielectric constant around 2.1(1) and reduced further below 2.0 by heat treatments.

• Smart Media Journal
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• v.2 no.1
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• pp.31-36
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• 2013

A robust and efficient text extraction is very important for an accuracy of Optical Character Recognition (OCR) systems. Natural scene images with degradations such as uneven illumination, perspective distortion, complex background and multi color text give many challenges to computer vision task, especially in text extraction. In this paper, we propose a method for extraction of the text in signboard images based on a combination of mean shift algorithm and weighting scheme of hue and saturation in HSI color space for clustering algorithm. The number of clusters is determined automatically by mean shift-based density estimation, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. Weighting scheme of hue and saturation is used for formulation a new distance measure in cylindrical coordinate for text extraction. The obtained experimental results through various natural scene images are presented to demonstrate the effectiveness of our approach.

• 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%.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.135-145
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• 2023

Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38% as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters

• ETRI Journal
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• v.38 no.4
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• pp.703-713
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• 2016

In this paper, we present a segmentation-based method for offline Farsi handwritten word recognition. Although most segmentation-based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub-words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub-words. Through the extraction of the number of sub-words in each word, and labeling the position of each sub-word (beginning/middle/end), many of the sub-word classifiers can be pruned, and a few remaining sub-word classifiers can be evaluated during the sub-word recognition stage. The candidate sub-words are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1837-1851
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• 2016

Newspaper reader mobile applications using text-to-speech (TTS) function enable blind people to read newspaper contents. But, tables cannot be easily read by the reader program because most of the tables are stored as images in the contents. Even though we try to use OCR (Optical character reader) programs to recognize letters from the table images, it cannot be simply applied to the table reading function because the table structure is unknown to the readers. Therefore, identification of exact location of each table cell that contains the text of the table is required beforehand. In this paper, we propose an efficient image processing algorithm to recognize all the cells in tables by identifying columns and rows in table images. From the cell location data provided by the table column and row identification algorithm, we can generate table structure information and table reading scenarios. Our experimental results with table images found commonly in newspapers show that our cell identification approach has 100% accuracy for simple black and white table images and about 99.7% accuracy for colored and complicated tables.