--- tags: 實習日記 --- [TOC] # 實習日記12/5~12/9 ## 篩選照片 1. 特別車牌 ![](https://i.imgur.com/nzQcaqy.jpg) 2. 歪斜車牌 ![](https://i.imgur.com/Y7CbP9l.png) 3. 夜晚車牌 ![](https://i.imgur.com/qIhrt6e.jpg) ![](https://i.imgur.com/VynN3wm.jpg) 4. 被後車廂擋住 ![](https://i.imgur.com/UFJERfR.jpg) 5. 車沒停好 ![](https://i.imgur.com/oWYKrbA.jpg) 6. 總結 - 歪斜:147張 - 特別車牌:30張 ## Label ![](https://i.imgur.com/cRD3Y9Q.jpg) ## train model 1. 訓練數據生成 - https://github.com/Belval/TextRecognitionDataGenerator - 結果![](https://i.imgur.com/NbTghwd.png) 2. 學習數據轉換 - https://github.com/DaveLogs/TRDG2DTRB 3. 訓練模型 - https://github.com/clovaai/deep-text-recognition-benchmark - 資料準備好後,還要去選學習模型,EacyOCR用'None-VGG-BiLSTM-CTC' 4. 測試模型 ## deep-text-recognition-benchmark 1. Transformation(轉正) - Transformation (Trans.) normalizes the input text image using the Spatial Transformer Network (STN) to ease downstream stages. 2. Feature extraction(特徵選取) - Feature extraction (Feat.) maps the input image to a representation that focuses on the attributes relevant for character recognition, while suppressing irrelevant features such as font,color, size, and background. 3. Sequence modeling - Sequence modeling (Seq.) captures the contextual information within a sequence of characters for the next stage to predict each character more robustly, rather than doing it independently. 4. Prediction (Pred.) - Prediction (Pred.) estimates the output character sequence from the identified features of an image. ### 參考資料 1. https://www.youtube.com/watch?v=m9RXvCl8wZ8&list=PL3unhkZOa0fY4e3VqQoe4qfMhlmCUW5O7&index=18 2. https://www.youtube.com/watch?v=-j3TbyceShY 3. https://www.youtube.com/watch?v=9_SRXdO9EC4 4. https://zhuanlan.zhihu.com/p/353385040 5. https://zhuanlan.zhihu.com/p/400270506