• 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 Advanced Technology Convergence
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• v.2 no.4
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• pp.7-11
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• 2023

In this study, we presented a method to recognize vehicle license plates using CNN techniques. A vehicle plate is normally used for the official identification purposes by the authorities. Most regular Optical Character Recognition (OCR) techniques perform well in recognizing printed characters on documents but cannot make out the registration number on the number plates. Besides, the existing approaches to plate number detection require that the vehicle is stationary and not in motion. To address these challenges to number plate detection we make the following contributions. We create a database of captured vehicle number plate's images and recognize the number plate character using Convolutional Neural Networks. The results of this study can be usefully used in parking management systems and enforcement cameras.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.163-170
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• 2022

With the advent of the 4th Industrial Revolution, solutions that apply AI technology are being actively developed. Since 2017, the introduction of business automation solutions using AI-based Robotic Process Automation (RPA) has begun in the financial sector and insurance companies, and recently, it is entering a time when it spreads past the stage of introducing RPA solutions. Among the business automation using these RPA solutions, it is very important how accurately textual information in the document is recognized for business automation using various documents. Such character recognition has recently increased its accuracy by introducing deep learning technology, but there is still no recognition model with perfect recognition accuracy. Therefore, in this paper, we checked how much accuracy is improved when pre- and post-processor technologies are applied to deep learning-based character recognition engines, and implemented RPA recognition engines and linkage technologies.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.100-108
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• 2016

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1772-1773
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• 2011

현대기술의 발전에 따라 계측기술 또한 다양하고 복잡하게 변모하였으며, 정밀측정기기의 교정 업무에서는 복잡하고 정확한, 반복적이며 계속적인 데이터 취득을 요구한다. 또한 여러 장비를 사용할 경우, 장시간 소요되는 데이터 취득과 정확한 계측기 사용법 및 고도의 관련기술을 필요로 한다. 그러므로 컴퓨터를 이용 한 계측장비 제어로 측정에 필요한 시간을 최대한 단축하고, 개인오차를 제거할 수 있는 동일한 결과와 쉽게 데이터를 취득할 수 있도록 측정자동화가 필요하다.

• 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.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.24-31
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• 2012

This paper introduce a technique for Word segmentation for the handwritten recognition of Urdu script. Word segmentation or word tokenization is a primary technique for understanding the sentences written in Urdu language. Several techniques are available for word segmentation in other languages but not much work has been done for word segmentation of Urdu Optical Character Recognition (OCR) System. A method is proposed for word segmentation in this paper. It finds the boundaries of words in a sequence of ligatures using probabilistic formulas, by utilizing the knowledge of collocation of ligatures and words in the corpus. The word identification rate using this technique is 97.10% with 66.63% unknown words identification rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.194-196
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• 2016

본 논문에서는 안드로이드 환경에서 글자 인식을 위한 전처리 과정으로 입력 영상에서 글자 영역만을 추출하는 기법을 제안한다. 대부분의 글자 인식 어플리케이션에서 글자를 인식하는 방법은 RoI(Region of Interest)에 인식하려는 글자를 위치시켜 놓고 사용자가 촬영함으로써 진행된다. 하지만 촬영된 영상 그대로를 인식에 사용하기 때문에 잡음 및 글자가 아닌 영역들을 글자로 인식하는 문제 등으로 인하여 인식률이 현저히 떨어진다. 제안하는 기법에서는 MSER(Maximally Stable Extremal Regions) 기법을 통해 각각의 글자를 추출한 후, 글자의 특성을 이용하여 글자 영역만을 추출한다. 기법의 성능 평가는 무료 OCR(Optical Character Recognition) 엔진인 Tesseract-OCR을 통해 글자 인식률을 비교하였으며, 제안하는 기법을 적용한 글자 인식 시스템이 적용하지 않은 시스템보다 글자의 인식률이 향상되는 것을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.500-503
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• 2012

스마트폰은 폭발적인 성장세와 더불어 어플리케이션의 시장규모가 크게 증대될 것으로 예상된다. 본 논문에서는 직접 사진을 캡처 시킨 문자들을 텍스트로 변환시켜주고 데이터베이스를 이용해서 그 문자들의 의미를 표현하는 어플리케이션을 구현한다. 어플리케이션의 기능으로는 텍스트로 변환하고자 하는 사진을 찍거나 혹은 앨범에 있는 사진을 불러내어 텍스트로 변환을 시켜서 상대방에게 메시지나 블루투스 등을 이용하여 전송을 가능하게 해줄 수 있으며, 글꼴을 바꾼다거나 크기 등 편집 기능까지 추가적으로 가능하도록 할 수 있다. 이 어플리케이션으로 인한 효과로 인터넷이나 거리를 돌아다니다가 원하는 문구가 있으면 바로 원하는 상대방에게 쉽고 간편하고 빠르게 전달할 수 있는 편리함 등이 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.167-170
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• 2022

본 논문에서는 화장품의 효율적 구매를 위한 화장품 성분표를 분석하고 정보를 전달하는 기능의 시스템을 제안한다. 이 시스템에서는 화장품 성분표에 최적화시킨 OCR (Optical Character Recognition) 모델을 사용해 화장품 성분표를 촬영한 영상에서 인식한 문자 데이터를 추출한다. 이 문자 데이터를 통해 얻은 화장품 성분이 사용자 피부 유형에 적합한지 구축된 데이터베이스와의 비교를 통해 소비자에게 최종 전달된다. 200개의 화장품 성분표 영상을 사용해 제안하는 화장품 성분표 분석 모델의 성능을 평가한 결과 80.348%의 정확도를 보였다.