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題名 基於電腦視覺技術的書法美學評估機制
作者 廖文宏
貢獻者 資訊科學系
關鍵詞 美感評估;書法;楷書;筆畫分析;結構分析;機器學習
Aesthetic evaluation; Chinese calligraphy; Regular script; Stroke analysis; Structural analysis; Machine learning
日期 2016
上傳時間 22-十二月-2017 17:15:57 (UTC+8)
摘要 中文書法經過了長久歷史的演變,已不單用來記錄事物,而轉化成為一種藝術的表現形式。從古至今,有眾多書法大家和美學家撰寫書法專書,然而中文書法理論大多講述較抽象的技法,且在相關文獻鮮少之情況下難以具體將美感量化。本研究的目的為以電腦視覺角度解析中文書法筆畫與結構,找出影響書法美觀程度的視覺元素,並加以量化分析,透過機器學習機制,使電腦具有基本的書法鑑賞能力。有別於過去研究,我們提出6種描述整體楷書書法作品美感的特徵,包含排版工整度、字距掌握度、文字偏移程度、文字書寫大小穩定度、筆畫風格一致程度以及筆畫平滑程度。本研究蒐集書法比賽和素人作品共100張,每張皆經由一般母語為中文之受測者的評估,並且將得到的評分作為樣本的標籤,透過SVM辨識3個級別和5個級別的樣本,兩者皆有好的辨識效果。再者,我們將辨識結果轉換成美感分數,亦能真實呼應人工評分。透過我們的研究成果,期望能提供書法初學者在書法創作上的基礎參考標準。
After a long history of evolution, Chinese calligraphy has transformed from a tool for writing to a unique form of art. Many publications regarding calligraphy writing techniques and appreciation have emerged along the way. Although the theory of Chinese calligraphy aesthetics is profound, it is difficult to define measures to quantify ”beauty” or ”taste” of calligraphic work. The objective of this work is to explore and extract relevant visual features for aesthetic evaluation of Chinese calligraphy using computer vision and machine learning techniques. Specifically, six global visual features have been proposed to describe the quality of calligraphy written in regular script, including layout, text spacing, text orientation, size stability, style uniformity and stroke smoothness. Together with local statistics obtained from each individual character, we employ support vector machine (SVM) to categorize the works into three or five levels of expertise. In both cases, good recognition results have been achieved. Furthermore, an aesthetic score can be obtained by converting the classification result with weighting factors. We hope that the evaluation results can assist beginners in identifying flaws in their writings and provide constructive suggestions to improve their skills in Chinese calligraphy.
關聯 執行起迄:2016/08/01~2017/07/31
105-2221-E-004-012
資料類型 report
dc.contributor 資訊科學系zh_Tw
dc.creator (作者) 廖文宏zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 22-十二月-2017 17:15:57 (UTC+8)-
dc.date.available 22-十二月-2017 17:15:57 (UTC+8)-
dc.date.issued (上傳時間) 22-十二月-2017 17:15:57 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/115323-
dc.description.abstract (摘要) 中文書法經過了長久歷史的演變,已不單用來記錄事物,而轉化成為一種藝術的表現形式。從古至今,有眾多書法大家和美學家撰寫書法專書,然而中文書法理論大多講述較抽象的技法,且在相關文獻鮮少之情況下難以具體將美感量化。本研究的目的為以電腦視覺角度解析中文書法筆畫與結構,找出影響書法美觀程度的視覺元素,並加以量化分析,透過機器學習機制,使電腦具有基本的書法鑑賞能力。有別於過去研究,我們提出6種描述整體楷書書法作品美感的特徵,包含排版工整度、字距掌握度、文字偏移程度、文字書寫大小穩定度、筆畫風格一致程度以及筆畫平滑程度。本研究蒐集書法比賽和素人作品共100張,每張皆經由一般母語為中文之受測者的評估,並且將得到的評分作為樣本的標籤,透過SVM辨識3個級別和5個級別的樣本,兩者皆有好的辨識效果。再者,我們將辨識結果轉換成美感分數,亦能真實呼應人工評分。透過我們的研究成果,期望能提供書法初學者在書法創作上的基礎參考標準。zh_TW
dc.description.abstract (摘要) After a long history of evolution, Chinese calligraphy has transformed from a tool for writing to a unique form of art. Many publications regarding calligraphy writing techniques and appreciation have emerged along the way. Although the theory of Chinese calligraphy aesthetics is profound, it is difficult to define measures to quantify ”beauty” or ”taste” of calligraphic work. The objective of this work is to explore and extract relevant visual features for aesthetic evaluation of Chinese calligraphy using computer vision and machine learning techniques. Specifically, six global visual features have been proposed to describe the quality of calligraphy written in regular script, including layout, text spacing, text orientation, size stability, style uniformity and stroke smoothness. Together with local statistics obtained from each individual character, we employ support vector machine (SVM) to categorize the works into three or five levels of expertise. In both cases, good recognition results have been achieved. Furthermore, an aesthetic score can be obtained by converting the classification result with weighting factors. We hope that the evaluation results can assist beginners in identifying flaws in their writings and provide constructive suggestions to improve their skills in Chinese calligraphy.en_US
dc.relation (關聯) 執行起迄:2016/08/01~2017/07/31zh_TW
dc.relation (關聯) 105-2221-E-004-012zh_TW
dc.subject (關鍵詞) 美感評估;書法;楷書;筆畫分析;結構分析;機器學習zh_TW
dc.subject (關鍵詞) Aesthetic evaluation; Chinese calligraphy; Regular script; Stroke analysis; Structural analysis; Machine learningen_US
dc.title (題名) 基於電腦視覺技術的書法美學評估機制_TW
dc.type (資料類型) report-