• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.51-60
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• 2020

Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.

• International Journal of Contents
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• v.8 no.3
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• pp.83-93
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• 2012

In this paper, we propose a novel method that automatically generates real character images to familiarize existing OCR systems with new fonts. At first, we generate synthetic character images using a simple degradation model. The synthetic data is used to train an OCR engine, and the trained OCR is used to recognize and label real character images that are segmented from ideal document images. Since the OCR engine is unable to recognize accurately all real character images, a substring matching method is employed to fix wrongly labeled characters by comparing two strings; one is the string grouped by recognized characters in an ideal document image, and the other is the ordered string of characters which we are considering to train and recognize. Based on our method, we build a system that automatically generates 2350 most common Korean and 117 alphanumeric characters from new fonts. The ideal document images used in the system are postal envelope images with characters printed in ascending order of their codes. The proposed system achieved a labeling accuracy of 99%. Therefore, we believe that our system is effective in facilitating the generation of numerous character samples to enhance the recognition rate of existing OCR systems for fonts that have never been trained.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.365-379
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• 2008

In this paper we consider technological requirements of the on-channel repeater to broadcast the terrestrial digital multimedia broadcasting (T-DMB) signals using single frequency networks (SFN) and propose the configuration and implementation method of the equalization on-channel repeater (OCR) that meet such requirements. The proposed equalization OCR not only has short time delay, but shows high output power and good quality of output signal by removing a feedback signal due to incomplete antenna isolation and multipath signal existing between the main transmitter and the OCR. In addition, computer simulations and laboratory tests results are provided to figure out performance of the proposed equalization OCR.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1065-1070
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• 2009

This paper presents the new technique of modeling using Dynamic Link Library(DLL) in ElectroMagnetic Transients Program - Restructured Version(EMTP-RV) in which we have simplified the procedures of OverCurrent Relay(OCR) modeling. The DLL function is designed to allow EMTP-RV users to develop advanced program model modules and interface them directly and intimately with the EMTP-RV engine. The modeled OCR is verified by simulating the various fault cases in the distribution system. Also, the performance for the modeling of OCR using DLL is compared with that of the method using the control components of EMTP-RV and using EMTP/MODELS. The results show the validity of modeled OCR and the effectiveness of the method using DLL function.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.438-440
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• 2021

With the development of machine learning, artificial intelligence techniques are being applied in various fields. Among these fields, there is an OCR technique that converts characters in images into text. The tesseract developed by HP is one of those techniques. However, the recognition rate for recognizing characters in images is still low. To this end, we try to improve the conversion rate of the text of the image through the post-processing process that recognizes the context using the ontology.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.563-564
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• 2015

Recently, distributed generation using renewable energy resources have increased due to the limitation of conventional energy. To utilize these energy source effectively, a method of applying the energy storage system(ESS) in distribution system has been considered. In this paper, we simulated the one line-to-ground fault in power system with ESS. Based on these simulations, we analyzed the effect on Over Current Relay(OCR) operation. As a result, ESS operation modes result fault current fluctuation. Thus, OCR need to reset the pick up current. This paper analyze effect of ESS in distribution system according to OCR setting by using ElectroMagnetic Transient Program(EMTP).

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.15-20
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• 2019

As the machine learning research has developed, the field of translation and image analysis such as optical character recognition has made great progress. However, video translation that combines these two is slower than previous developments. In this paper, we develop an image translator that combines existing OCR technology and translation technology and verify its effectiveness. Before developing, we presented what functions are needed to implement this system and how to implement them, and then tested their performance. With the application program developed through this paper, users can access translation more conveniently, and also can contribute to ensuring the convenience provided in any environment.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.833-841
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• 1999

The texts constructed with Optical Character Recognition(OCR) contain more errors than those constructed with keyboard typing. Therefore, in order to retrieve useful information from OCR texts, we need to develop an effective automatic indexing method. In this paer, we investigate automatic indexing methods that can retrieve information effectively from Korean OCR text database with the character-level recognition ratio of 90%. Experimental result shows that 2-gram indexing provides similar retrieval effectiveness of morpheme-based indexing for the Korean OCR text database.