yiran-THU / EAST-caffe

EAST text detection CAFFE reimplement

EAST: An Efficient and Accurate Scene Text Detector

Introduction

This is a CAFFE re-implementation of EAST: An Efficient and Accurate Scene Text Detector.

thanks to these project:

• https://github.com/argman/EAST
• https://github.com/YukangWang/TextField
• https://github.com/chuanqi305/MobileNet-SSD

The features are summarized blow:

• OpenCV_DNN/ CAFFE inference demo
• Only RBOX part is implemented.
• Use VGG/ MobileNet_v2 as backbone,
• NCNN/ MNN deploy support, Use NCNN int8 quantization, the model size can be 2M or less. Very suitable for deploy on Mobile devices.

Please cite his paper if you find this useful.

Contents

1. Installation
2. Download
3. Train
4. Demo
5. Test
6. Train
7. Examples

Installation

1. Any version of caffe version > 1.0 should be ok. (suggest use the https://github.com/weiliu89/caffe/tree/ssd)

Download

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

Train

If you want to train the model, you should provide the dataset path, in the dataset path, the images and the gt text files should be separated into two filefolders as shown as below:

train_images\   train_gts\   test_images\   test_gts\

and the gts content format is

x1,y1,x2,y2,x3,y3,x4,y4,recog_results
and run

python train.py --gpu 0 --initmodel my_model.caffemodel

If you have more than one gpu, you can pass gpu ids to gpu_list(like --gpu_list=0,1,2,3)

Demo

python demo.py