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:

• https://github.com/WenmuZhou/PAN.pytorch

The features are summarized blow:

• Use resnet18/resnet50/shufflenetV2 as backbone.

Contents

1. Installation
2. Download
3. Train
4. Predict
5. Eval
6. Demo

Installation

1. pytorch 1.1.0

Download

1. ShuffleNet_V2 Models trained on ICDAR 2013+2015 (training set)

https://pan.baidu.com/s/1Um0wzbTFjJC0jdJ703GR7Q

or https://mega.nz/#!WdhxXAxT!oGURvmbQFqTHu5hljUPdbDMzI75_UO2iWLaXX5dJrDw

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

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