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• Main Authors: Yi-Cheng Chen, 陳奕誠
• Other Authors: Yong-Bin Zheng
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/887z76

碩士 === 國立中央大學 === 資訊工程學系 === 107 === In the past, the success or failure of optical character recognition (OCR) is often inextricably linked to the extraction of features. If you can’t find an effective feature, the result will not be as preferable as expected. However, the improvement of hardware devices and computing power have made deep learning become a hot field in recent years due to its ability to automatically extract features and effectiveness to find good features to enhance the recognition ability of optical character recognition. According to IBM estimates, about $2.5 trillion a year has been spent on storing non-digital files by converting them into digital files by manual typing. If it is possible to improve the recognition rate of OCR to certain acceptable standard, then it can save time and reduce costs. Besides, there aren’t any tools with a recognition rate of 100% today because there are many different sources of identification images, such as scanned files, camera photo noise, complex typography, text and background colors, large and small icons, different languages and fonts that will greatly affect the recognition results. The purpose of this paper is to find a way to effectively improve the OCR software recognition rate. We used screenshots of webpages that have better corrected images and don’t have noise. The computer font is True Type Font, so the screenshots may be different even if the same page is on different screens. The result of testing indicates Google Vision, a cloud service, has better recognition rate than other software. However, many factories that demand OCR don’t connect to the Internet, so we choose Tesseract 4.0 which is an open source. The findings of this paper show that with its low recognition rate, the pre-processing of Tesseract 4.0 has better improved its recognition rate than its training. The poor result of its training is mainly caused by complex typography and different text sizes.