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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳祝嵩 | zh_TW |
| dc.contributor.advisor | Chu-Song Chen | en |
| dc.contributor.author | 陳冠盛 | zh_TW |
| dc.contributor.author | Guan-Sheng Chen | en |
| dc.date.accessioned | 2023-08-09T16:36:17Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-09 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-27 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88336 | - |
| dc.description.abstract | 場景文字領域的任務旨在偵測與辨認生活周遭的文字。然而，在收集和標註場景文字的資料集時，需要大量的時間和精力，因此在標注過程中使用高效且可靠的工具，以及適當的安排標注流程是不可或缺的。我們針對繁體中文環境下的場景文字資料集之標注任務，建立了一個標注系統。在此系統中，我們嘗試使用持續學習的技術來快速調整自動提示模型，同時針對中文類別數過多，導致字元分布不平衡的問題引入長尾技術。此外，我們討論了不同的標注流程下，自動提示模型的性能。為了比較這些技術與流程的優劣，我們也提出了一個適用於標注系統的評估方式。最後，我們在系統中引入了基礎模型，以及分析不同工具對標注者標注時間的影響。透過這些技術的應用，將能實現更高效率的資料集標注。 | zh_TW |
| dc.description.abstract | Scene-text models aim to detect and recognize texts in our daily life environment. However, it requires lots of time and efforts to build a scene text dataset annotated by humans. Therefore, we often need to provide the detection and recognition results based on the currently available model to reduce the effort of labeling. To this end, we develop an annotation system for annotating Traditional Chinese scene text datasets. In this system, we try to use continual learning techniques to quickly adjust the automatic labeling tools and attempt to adapt existing long-tail techniques into our system to address imbalanced character distribution in Chinese characters. In addition, we discuss the performance of the automatic labeling tools under different annotation processes. To compare the strengths and weaknesses of these techniques and processes, we also propose a new evaluation protocol for the annotation system. Finally, we introduce the foundation model combined in our system as well as analyze the impact of different tools on the annotation time. By incorporating these techniques, we can achieve a more efficient annotation process. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:36:17Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-09T16:36:17Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iii Abstract iv Contents vi List of Figures ix List of Tables x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Thesis Organization 3 Chapter 2 Related Works 5 2.1 Continual Learning and Incremental Learning 5 2.2 Long-tail Learning 7 2.3 Missing Label in Object Detection 9 2.4 Scene Text 9 2.4.1 Datasets 10 2.4.2 Text Detection 11 2.4.3 Text Recognition 12 2.4.4 Text Spotting 13 2.4.5 Text Annotation System 13 Chapter 3 Annotation Process Benchmark 15 3.1 Evaluation Protocol 15 3.2 One-Stage vs. Two-Stage Annotation Process 17 3.3 Traditional Chinese Scene Text Dataset 18 3.4 Evaluation Metrics 22 3.4.1 Scene Text Detection with Continual Learning 22 3.4.2 Scene Text Recognition with Continual Learning 23 3.4.3 One-Stage vs. Two-Stage Annotation Process 24 Chapter 4 Experiments 26 4.1 Scene Text Detection 26 4.1.1 Implementation Details 26 4.1.2 Results 28 4.2 Scene Text Recognition 31 4.2.1 Implementation Detail 31 4.2.2 Results 32 4.3 One-Stage Annotation vs. Two-Stage Annotation 34 4.3.1 Implementation Details 34 4.3.2 Results 35 Chapter 5 Annotation System Architecture 38 5.1 Overall Architecture 38 5.2 Annotation Time Experiments 40 Chapter 6 Conclusion 43 References 45 | - |
| dc.language.iso | en | - |
| dc.subject | 長尾問題 | zh_TW |
| dc.subject | 持續學習 | zh_TW |
| dc.subject | 標注系統 | zh_TW |
| dc.subject | 文字偵測 | zh_TW |
| dc.subject | 場景文字 | zh_TW |
| dc.subject | 文字辨識 | zh_TW |
| dc.subject | Text Detection | en |
| dc.subject | Annotation System | en |
| dc.subject | Scene Text | en |
| dc.subject | Text Recognition | en |
| dc.subject | Long-Tail Problem | en |
| dc.subject | Continual Learning | en |
| dc.title | 基於持續與雙階段學習的場景文字標註平台研究 | zh_TW |
| dc.title | Research on Constructing Scene Text Annotation Platform Using Continual Learning Technology in Two Stages | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 徐繼聖;葉梅珍;彭彥璁;陳彥仰 | zh_TW |
| dc.contributor.oralexamcommittee | Gee-Sern Hsu;Mei-Chen Yeh;Yan-Tsung Peng;Mike Y. Chen | en |
| dc.subject.keyword | 場景文字,文字偵測,文字辨識,標注系統,持續學習,長尾問題, | zh_TW |
| dc.subject.keyword | Scene Text,Text Detection,Text Recognition,Annotation System,Continual Learning,Long-Tail Problem, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202302185 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-28 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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