Real-time-Text-Detection

PyTorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

Difference between thesis and this implementation

  1. Use dice loss instead of BCE(binary cross-entropy) loss.

  2. Use normal convolution rather than deformable convolution in the backbone network.

  3. The architecture of the backbone network is a simple FPN.

  4. Have not implement OHEM.

  5. The ground truth of the threshold map is constant 1 rather than 'the distance to the closest segment'.

Introduction

thanks to these project:

The features are summarized blow:

  • Use resnet18/resnet50/shufflenetV2 as backbone.

Installation

  1. pytorch 1.1.0

Download

  1. ShuffleNet_V2 Models trained on ICDAR 2013+2015 (training set)
    https://pan.baidu.com/s/1Um0wzbTFjJC0jdJ703GR7Q

Train

  1. modify genText.py to generate txt list file for training/testing data

  2. modify config.json

  3. run

python train.py

Predict

  1. run
python predict.py

Eval

run

python eval.py

Examples

contour

bbox

Todo

  • [ ] MobileNet backbone

  • [ ] Deformable convolution

  • [ ] tensorboard support

  • [ ] FPN --> Architecture in the thesis

  • [ ] Dice Loss --> BCE Loss

  • [ ] threshold map gt use 1 --> threshold map gt use distance （Use 1 will accelerate the label generation）

  • [ ] OHEM

  • [ ] OpenCV_DNN inference API for CPU machine

  • [ ] Caffe version (for deploying with MNN/NCNN)

  • [ ] ICDAR13 / ICDAR15 / CTW1500 / MLT2017 / Total-Text

GitHub