【Paper Reading】【TextBoxess: A Fast Text Detector with a Single Deep Neural Network】
Introduction： 傳統文本檢測方法步驟： 1) character/word candidate generation：候選詞的產生 2) Candidate filtering：候選詞過濾 3) grouping：分組 弊端：需要保證每一個模型都正常工作，從而需要耗費大量精力調參和設計啓發式規則，這同樣也會減慢檢測速度 本文：通過一個端對端訓練的單一神經網絡直接預測單詞bounding
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相關文章
- 1. 論文筆記：TextBoxes: A Fast Text Detector with a Single Deep Neural Network
- 2. [論文筆記]TextBoxes A Fast Text Detector with a Single Deep Neural Network
- 3. 《TextBoxes: A Fast Text Detector with a Single Deep Neural Network》閱讀翻譯
- 4. #Paper Reading# A Neural Attention Model for Abstractive Sentence Summarization
- 5. 論文閱讀 | Towards a Robust Deep Neural Network in Text Domain A Survey
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- 7. 翻譯和筆記--FOTS: Fast Oriented Text Spotting with a Unified Network
- 8. Logistic-Regression-with-a-Neural-Network-mindset
- 9. [Paper Reading] Show and Tell: A Neural Image Caption Generator
- 10. 目標檢測--A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection