《Recursive Recurrent Nets with Attention Modeling for OCR in the Wild》 筆記
該文提出了一個基於注意力模型的遞歸循環神經網絡模型（R2AM),解決在在無字典約束的條件下，對自然場景文字進行識別.提出的模型主要有以下幾個優點：（1）採用了循環的CNN網絡，可以更加有效和準確地提取圖像特徵；（2）在一個隱式的字符級別識別模型中嵌入一個RNN網絡可以避免去使用N-元分析法；（3）採用軟注意力機制可以允許模型選擇性的利用特徵，並且運行採用標準的BP算法進行端到端的訓練。
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相關文章
- 1. OCR之R^2AM(Recursive Recurrent Nets with Attention Modeling for OCR in the Wild)
- 2. Attention in CV
- 3. Material Recognition in the Wild with the Materials in Context Database論文筆記
- 4. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 5. Attention-GAN for Object Transfiguration in Wild Images
- 6. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 7. Multilayer in-place learning networks for modeling functional layers in the laminar cortex筆記
- 8. 【論文筆記】Learning from Synthetic Data for Crowd Counting in the Wild
- 9. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 10. [LRFR] 論文筆記 Finding Tiny Faces in the Wild with Generative Adversarial Network