基於深度學習的中文手寫字辨識;Handwritten Chinese Characters Recognition Based on Deep Learning

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题名:	基於深度學習的中文手寫字辨識;Handwritten Chinese Characters Recognition Based on Deep Learning
作者:	李宣霈;Lee, Hsuan-Pei
贡献者:	資訊工程學系
关键词:	深度學習;中文手寫字;手寫字辨識;Deep Learning;Handwritten Chinese Chracters;Handwritten Characters Recognition
日期:	2019-08-22
上传时间:	2019-09-03 15:46:12 (UTC+8)
出版者:	國立中央大學
摘要:	在機器學習和深度學習還沒這麼熱門時，文字影像辨識這個領域就已經有不少研究和討論，像OCR（Optical Character Recognition）的技術發展已經算是很成熟。隨著近年來深度學習飛快地發展，文字影像辨識也同樣獲益，越來越多搭配深度學習方法來做文字影像辨識的研究一一出爐。英數字的辨識在近年來已經逐漸成熟，但中文字礙於本身文字結構比較複雜，加上中文字庫之龐大，使得中文字辨識的技術即使搭配了深度學習，其成熟度仍比不上英數字的辨識。除了中文字本身辨識難度比英數字高，不同人的手寫風格又不一樣，如果同一份文件有來自不同人的手寫字體，文字辨認的難度就又更高了。因此本篇論文研究的重點在於，如果使用生成網路，大量生成不同風格的字體，加到中文手寫字的資料庫，和未加入多種不同風格的手寫字體的資料庫相比，是否能夠有更好的辨認效果？ ;Character recognition has already been a popular research field even when machine learning and deep learning haven’t been discussed frequently. For example, the technique of OCR(Optical Character Recognition) has already been quite mature. Along with the development of machine learning and deep learning these years, the research of character recognition has also made a great leap by using deep learning. English characters and digit recognition has already been quite mature. However, Chinese characters recognition hasn’t been as mature as English characters and digit recognition even if many researches were based on deep learning since the structure of Chinese characters is more complexed. In addition that the Chinese characters recognition is more difficult than English characters and digit recognition, due to the variance of the style of handwritten characters from one person to another person, handwritten characters is even more difficult to be detected or recognized if there are more than one style of handwritten characters on a piece of paper. Therefore, the purpose of this research is to find out whether the multi-style handwritten Chinese characters dataset can do better job on character detection and recognition compared to one-style or few-style handwritten Chinese characters dataset.
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