深度学习
  • 前言
  • 第一章：经典网络
    • ImageNet Classification with Deep Convolutional Neural Network
    • Very Deep Convolutional Networks for Large-Scale Image Recognition
    • Going Deeper with Convolutions
    • Deep Residual Learning for Image Recognition
    • PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
    • Squeeze-and-Excitation Networks
    • Densely Connected Convolutional Networks
    • SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE
    • MobileNet v1 and MobileNet v2
    • Xception: Deep Learning with Depthwise Separable Convolutions
    • Aggregated Residual Transformations for Deep Neural Networks
    • ShuffleNet v1 and ShuffleNet v2
    • CondenseNet: An Efficient DenseNet using Learned Group Convolution
    • Neural Architecture Search with Reinforecement Learning
    • Learning Transferable Architectures for Scalable Image Recognition
    • Progressive Neural Architecture Search
    • Regularized Evolution for Image Classifier Architecture Search
    • 实例解析：12306验证码破解
  • 第二章：自然语言处理
    • Recurrent Neural Network based Language Model
    • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
    • Neural Machine Translation by Jointly Learning to Align and Translate
    • Hierarchical Attention Networks for Document Classification
    • Connectionist Temporal Classification : Labelling Unsegmented Sequence Data with Recurrent Neural Ne
    • About Long Short Term Memory
    • Attention Is All you Need
    • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  • 第三章：语音识别
    • Speech Recognition with Deep Recurrent Neural Network
  • 第四章：物体检测
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
    • Fast R-CNN
    • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
    • R-FCN: Object Detection via Region-based Fully Convolutuional Networks
    • Mask R-CNN
    • You Only Look Once: Unified, Real-Time Object Detection
    • SSD: Single Shot MultiBox Detector
    • YOLO9000: Better, Faster, Stronger
    • Focal Loss for Dense Object Detection
    • YOLOv3: An Incremental Improvement
    • Learning to Segment Every Thing
    • SNIPER: Efficient Multi-Scale Training
  • 第五章：光学字符识别
    • 场景文字检测
      • DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
      • Detecting Text in Natural Image with Connectionist Text Proposal Network
      • Scene Text Detection via Holistic, Multi-Channel Prediction
      • Arbitrary-Oriented Scene Text Detection via Rotation Proposals
      • PixelLink: Detecting Scene Text via Instance Segmentation
    • 文字识别
      • Spatial Transform Networks
      • Robust Scene Text Recognition with Automatic Rectification
      • Bidirectional Scene Text Recognition with a Single Decoder
      • multi-task learning for text recognition with joint CTC-attention
    • 端到端文字检测与识别
      • Reading Text in the Wild with Convolutional Neural Networks
      • Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework
    • 实例解析：字符验证码破解
    • 二维信息识别
      • 基于Seq2Seq的公式识别引擎
      • Show and Tell: A Neural Image Caption Generator
      • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
  • 第六章：语义分割
    • U-Net: Convolutional Networks for Biomedical Image Segmentation
  • 第七章：人脸识别
    • 人脸检测
      • DenseBox: Unifying Landmark Localization with End to End Object Detection
      • UnitBox: An Advanced Object Detection Network
  • 第八章：网络优化
    • Batch Normalization
    • Layer Normalization
    • Weight Normalization
    • Instance Normalization
    • Group Normalization
    • Switchable Normalization
  • 第九章：生成对抗网络
    • Generative Adversarial Nets
  • 其它应用
    • Holistically-Nested Edge Detection
    • Image Style Transfer Using Convolutional Nerual Networks
    • Background Matting： The World is Your Green Screen
  • Tags
  • References
由 GitBook 提供支持
在本页

这有帮助吗？

场景文字检测

上一页第五章：光学字符识别下一页DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images

最后更新于4年前

DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
Detecting Text in Natural Image with Connectionist Text Proposal Network
Scene Text Detection via Holistic, Multi-Channel Prediction
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
PixelLink: Detecting Scene Text via Instance Segmentation