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

• International journal of advanced smart convergence
• /
• v.8 no.3
• /
• pp.95-101
• /
• 2019

Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.07a
• /
• pp.425-426
• /
• 2017

본 논문에서는 Google에서 지원하는 오픈소스인 Tesseract-OCR을 이용한 문자/음성 인식 및 번역 앱에 대해 제안한다. 최근 한국어를 포함한 외국어 인식과 번역기능을 이용한 다양한 스마트폰 앱이 개발되어 여행에 필수품으로 자리잡고 있다. 스마트폰의 카메라기능을 이용하여 촬영한 영상을 인식률을 높이도록 처리하고, Crop기능을 넣어 부분 인식기능을 지원하며, Tesseract-OCR의 train data를 보완하여 인식률을 높이고, Google 음성인식 API를 이용한 음성인식 기능을 통해 인식된 유사한 문장들을 선택하도록 하고, 이를 번역하고 보여주도록 개발하였다. 번역 기능은 번역대상 언어와 번역할 언어를 선택할 수 있고 기본적으로 영어, 한국어, 일본어, 중국어로 번역이 가능하다. 이 기능을 이용하여 차량번호 인식, 사진에 포함된 글자를 통한 검색 등 다양한 응용분야에 맞게 앱을 개발할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.606-607
• /
• 2017

디지털 정보 처리 및 습득 속도에 대한 관심이 높아지면서 이와 관련된 많은 연구가 수행되고 있지만 아날로그 정보에 대한 연구는 많이 부족하다. 따라서 본 논문에서는 자동으로 책을 넘기고 각 페이지의 시잔을 촬영하여 컴퓨터로 전송한 후에 Tesserac-OCR을 이용하여 이를 디지털화 하여 저장하고 사용자가 원하는 키워드가 존재하는 페이지를 찾아 출력하는 시스템을 설계 및 구현한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.615-618
• /
• 2019

이 모듈은 오픈소스인 Tesseract OCR 및 Open CV 라이브러리와 Raspberry Pi를 사용하여 저렴한 비용으로 구현합니다. 컴팩트한 사이즈로 사람이 직접 들고 움직이면서도 사용이 가능하며 사용자의 니즈에 따라서 한 곳에 위치하여도 사용 가능합니다. Open CV 라이브러리를 사용하여 이미지 이진화, 노이즈 필터링 후에 흑백 이미지를 만들고 윤곽선 검출 알고리즘을 통해서 번호판 영역을 추출하여 Tesseract OCR 엔진을 사용해서 차량 번호판이 추출된 이미지에서 차량 번호를 인식 합니다. 인식된 번호는 Tkinter 와 Python, 데이터베이스를 활용하여 구현된 GUI프로그램을 통해서 유료주차장(선불, 후불) 또는 아파트에서 사용할 수 있는 주차장 관리 서비스를 함께 제공합니다.

• Journal of Internet Computing and Services
• /
• v.20 no.4
• /
• pp.29-38
• /
• 2019

Currently, information society is rapidly changing and demands innovation and creativity in various fields. Therefore, the importance of mathematics, which can be the basis of creativity and logic, is emphasized. The purpose of this paper is to develop a math education application that can further expand the logical thinking of mathematics and allow voluntary learning to occur through the use of readily available teaching aid for children to form motivation and interest in learning. This paper provides math education applications using a smartphone and blocks for children. The main function of the application is to shoot with the camera and show the calculated values. When a child uses a block to make a formula and shoots a block using a camera, you can directly see the calculated value of your formula. The preprocessing process, text extraction, and character recognition of the photographed images have been implemented using OpenCV libraries and Tesseract-OCR libraries.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1209-1214
• /
• 2016

LPR(License plate recognition) is a kind of the most popular surveillance technology based on accompanied by a video and video within the optical character recognition. LPR need a many process. One is a localization of car license plates, license plate of size, space, contrast, normalized to adjust the brightness, another is character division for recognize the character optical character recognition to win the individual characters, character recognition, the other is phrase analysis of the shape, size, position by year, the procedure for the analysis by comparing the database of license plate having a difference by region. In this paper, describing the results of performance of license plate recognition S/W, which was implemented using EmguCV, find the location, using the tesseract OCR, which are well known to an optical character recognition engine of open source, the characters of the license plate image capturing angle of the plate, image size, brightness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.693-695
• /
• 2018

In this paper, we propose the improvement of Hangul OCR accuracy through deep learning. OCR is a program that senses printed and handwritten characters in an optical way and encodes them digitally. In the case of the most commonly used Tesseract OCR, the accuracy of English recognition is high. However, Hangul has lower accuracy because it has less learning data for a complex structure. Therefore, in this study, we propose a method to improve the accuracy of Hangul OCR by extracting the character region from the desired image through image processing and using deep learning using it as learning data. It is expected that OCR, which has been developed only by existing alphanumeric and several languages, can be applied to various languages.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.5
• /
• pp.951-959
• /
• 2017

In this paper, we design and implement an Android-based Hangul food menu recognition application that recognizes characters from images captured by a smart phone. Optical Character Recognition (OCR) technology is divided into preprocessing, recognition and post-processing. In the preprocessing process, the characters are extracted using Maximally Stable Extremal Regions (MSER). In recognition process, Tesseract-OCR, a free OCR engine, is used to recognize characters. In the post-processing process, the wrong result is corrected by using the dictionary DB for the food menu. In order to evaluate the performance of the proposed method, experiments were conducted to compare the recognition performance using the actual menu plate as the DB. The recognition rate measurement experiment with OCR Instantly Free, Text Scanner and Text Fairy, which is a character recognizing application in Google Play Store, was conducted. The experimental results show that the proposed method shows an average recognition rate of 14.1% higher than other techniques.

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