zj463261929 / textboxes

TextBoxes: A Fast Text Detector with a Single Deep Neural Network https://github.com/MhLiao/TextBoxes 基于SSD改进的文本检测算法，textBoxes_note记录了之前整理的笔记。

TextBoxes: A Fast Text Detector with a Single Deep Neural Network

Introduction

This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard nonmaximum suppression. For more details, please refer to our paper.

Citing TextBoxes

Please cite TextBoxes in your publications if it helps your research:

@inproceedings{LiaoSBWL17,
  author    = {Minghui Liao and
               Baoguang Shi and
               Xiang Bai and
               Xinggang Wang and
               Wenyu Liu},
  title     = {TextBoxes: {A} Fast Text Detector with a Single Deep Neural Network},
  booktitle = {AAAI},
  year      = {2017}
}

Contents

1. Installation
2. Download
3. Test
4. Train
5. Performance

Installation

1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT

git clone https://github.com/MhLiao/TextBoxes.git

cd TextBoxes

make -j8

make py

Download

1. Models trained on ICDAR 2013: Dropbox link BaiduYun link
2. Fully convolutional reduced (atrous) VGGNet: Dropbox link BaiduYun link
3. Compiled mex file for evaluation(for multi-scale test evaluation: evaluation_nms.m): Dropbox link BaiduYun link

Test

1. Download the ICDAR 2013 DataSet
2. Download the Models trained on ICDAR 2013
3. Modify the related paths in the "examples/TextBoxes/test_icdar13.py"
4. run "python examples/test_icdar13.py"
5. To multi-scale test, you should use "test_icdar13_multi_scale.py" and "evaluation_nms.m"

Train

1. Train about 50k iterions on Synthetic data which refered in the paper.
2. Train about 2k iterions on corresponding training data such as ICDAR 2013 and SVT.
3. For more information, such as learning rate setting, please refer to the paper.

Performance

1. Using the given test code, you can achieve an F-measure of about 80% on ICDAR 2013 with a single scale.
2. Using the given multi-scale test code, you can achieve an F-measure of about 85% on ICDAR 2013 with a non-maximum suppression.
3. More performance information, please refer to the paper and Task1 and Task4 of Challenge2 on the ICDAR 2015 website: http://rrc.cvc.uab.es/?ch=2&com=evaluation

Please let me know if you encounter any issues.

textboxes's Issues

textboxes LMDB

非常感谢您的工作！@zj463261929 将合成80k数据从gt.mat提取出来的脚本可以分享一下吗？您所说的voc_label_text_xml_for_ssd.py

你能在tensorflow上实现这个TextBoxes吗？

我找到了作者在tensorflow上的实现代码，但是他那个代码跑不通，感觉有问题。你能在tensooflow上实现一下吗？

关于环境配置的问题

作者您好，想请教您，关于您运行的环境配置问题，您可以介绍一下吗？我看您的note里面写“在100的服务器上用nvidia-docker从镜像gds/keras-th-tf-opencv中新建了caffe_ys容器。按照caffe的依赖文件，并编译GPU版本。”不太明白具体该如何操作，是Windows系统下的caffe编译配置吗？可以留下您的联系方式向您请教吗？