博碩士論文 108552011 詳細資訊




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姓名 郭彥明(Yan-Ming Guo)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 使用CRAFT模型於古文書文字切割
(Chinese Ancient Calligraphy Word Detection Based On CRAFT Model)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-7-21以後開放)
摘要(中) 在數位典藏的推廣應用中,對於中文古籍全文資料庫的建立,古籍文字切割與辨識是重要環節。光學字元辨識(Optical Character Recognition, OCR),需要切割出每一個文字的座標再加以辨識,切割文字錯誤時,辨識的結果也就不可能正確。因此文本的文字切割對於正確的文字識別有著重大的影響和價值,本研究我們設計了一個中文古籍文本的文字切割系統。我們設計的系統輸入影像為彩色掃瞄圖檔,我們使用CRAFT文字偵測模型對輸入影像進行文字偵測,找出文字像素範圍,加以切割出文字區塊圖片。
摘要(英) In the promotion and application of digital archives, for the establishment of the full-text database of Chinese ancient books, the text cutting and identification of ancient books is an important link. Optical Character Recognition (OCR) requires cutting out the coordinates of each character and then recognizing it. When the character is cut wrong, the recognition result cannot be correct. Therefore, the text cutting of the text has a significant impact and value on the correct text recognition. In this research, we have designed a text cutting system for Chinese ancient texts. The input image of the system we designed is a color scan image file. We use the CRAFT text detection model to detect the text of the input image, find the text pixel range and then cut out the text block image.
關鍵字(中) ★ 文字切割
★ 古文書法
關鍵字(英) ★ CRAFT
★ OCR
論文目次 目錄
摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章、緒論 1
1.1研究背景與動機 1
1.2 研究目標 2
1.3 論文架構 2
第二章、方法回顧 3
2.1 光學字元識別OCR(Optical Character Recognition) 3
2.1.1 文字辨識引擎Tesseract 3
2.2 CRAFT 文字偵測 4
2.2.1 神經網路模型架構 4
2.2.2 熱力值產生(字元區域分數和親和力分數) 5
2.2.3 弱監督學習 7
2.3 遷移學習 8
2.3.1新的訓練資料集小、與預訓練資料集相似度高 9
2.3.2新的訓練資料集小、與預訓練資料集相似度低 9
2.3.3新的訓練資料集大、與預訓練資料集相似度高 9
2.3.4新的訓練資料集大、與預訓練資料集相似度低 10
第三章、系統架構介紹與方法 11
3.1 MIAT系統設計方法論 11
3.2文字切割系統模型架構 12
3.3遷移學習訓練系統模型架構 14
3.3.1訓練資料集收集 16
3.3.2標記ROI字元框 18
3.3.3熱力圖生成 19
3.3.4 模型訓練經驗 20
第四章、實驗結果與分析 21
4.1 實驗環境 21
4.2 評估方法 21
4.2.1偵測率評估方法 22
4.2.2計算TP/FP/FN/TN各類別像素個數 22
4.2.3計算平均Recall與Precision 23
4.2.4計算F1 Score 23
4.3偵測性能實驗 24
4.3.1文字偵測率 24
4.3.2文字偵測IoU效能評估 24
第五章、結論與未來展望 27
5.1 結論 27
5.2 未來展望 27
參考文獻 28
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指導教授 陳慶瀚(Ching-Han Chen) 審核日期 2022-7-21
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