CRAFT-based Chinese words detector training

Description

Test environment:Ubuntu 16.04
Instruction: this is a sample of CRAFT training experiment in Chinese dataset, Datatang open source Chinese Mandarin Corpus
Update time of document: November 13,2019

Introdution of CRAFT

Implementation of CRAFT text detector paper. Our aim is training a Chinese detector with CRAFT,Here is offical inference project,Here is training project we reference. We did a little change in original training model and make a comparative experiment.

Installation

Install dependence(Recomend Anaconda)

Install anaconda first. Create conda environment

conda create -n CRAFT python=3.6

Activate conda environment

conda activate CRAFT

Install packages

pip install -r requirements.txt

training

General Overview

There are several folders and files under project, and their brief introductions are as follows：

Folder	Description	Remark
converts	Script directory	transform data format
module	Model scripts	model modules
net	Model scripts	VGG16 net backbone
utils	Script directory	inference&training utils
weights	Model weights	weights folder
compute_PPR.py	General scripts	analyze trained model
test_json.py	General scripts	generate demo results
train.py	General scripts	training program

Detailed explanation of training

Training phase could be separated into two parts: prepare datasets and run training script.

Download and decompression of data set

Download two datasets from urls below:
CTW official web
SynthText official web
SynthText project link Introduction CTW provides details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3850 unique ones annotated by experts in over 30000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc.
SynthText is a synthetically generated dataset, in which word instances are placed in natural scene images, while taking into account the scene layout.The dataset consists of 800 thousand images with approximately 8 million synthetic word instances. Each text instance is annotated with its text-string, word-level and character-level bounding-boxes.

Data preparation

Using follow scripts could converte dataset into specified format.
CTW dataset

python converts/CTW_convert.py

SynthText dataset

python converts/synth_convert.py

Training details

In Originl training project author added sigmoid function in network. Contrast experiments we have done prove that:

Sigmoid function can bring bigger loss during training phase.
It is easiler to fall in local optimum.
It can effectively shrinking training time. We used linear function instead sigmoid and amplify input image size to 800x800. Datasets have two parts: English data and Chinese data. Training phase will combine two parts of data and English data will be limited under manmade rate of Chinese and English.

In our experience, set batch size to 8 is fine on NVIDIA 1080TI/Tesla V100 and reducing image size will lead a bigger batch size. During training phase, learning rate multiply by 0.64 after 30K steps will hold a stable decay of loss. Initial learning has a close correlation with batch size, bigger batch size bring a bigger learning rate. In our experence, learning rate set 0.00001 is fine when batch size equal to 8. Run:

python train.py

Inference

We provide three kinds detector for inference phase: character level, word level, line level. You can choose one of them in inference_util.py Get demo, heatmap and json file by running:

python test_json.py

Compute PPR(Pixel Rate) PPR(Pixel Pass Rate) and MPD(Mean Pixel Difference) are indicators to measure how close the predict box and ground truth are.

python compute_PPR.py

Summary

Using CTW and SynthText for training and separate them for testing. i. Official is official release model, Original is original training model and Modification is modificate training model.

OCR Detection Training	\	Official	Official	Original	Original	Modification	Modification
Hardware	\	\	\	1 1080Ti	1 1080Ti	1 Tesla v100	1 Tesla v100
Data size(Test)	\	SynT	CTW	SynT	CTW	SynT	CTW
AP	\	\	\	\	\	\	\
Pixels Pass Rate(PPR)[[email protected]:.95]	1	6.1%	0%	3.1%	0%	4.1%	0%
	2	32.6%	1.4%	22.5%	1.1%	26.8%	1.2%
	3	52.6%	5.6%	44.7%	4.3%	49.0%	5.0%
	4	62.9%	11.9%	59.3%	10.2%	62.5%	12.1%
	5	69.1%	18.0%	69.3%	17.5%	70.7%	21.9%
	6	73.4%	22.7%	75.8%	23.7%	76.0%	28.2%
	7	76.5%	26.0%	80.1%	28.7%	79.6%	32.9%
	8	79.1%	28.5%	83.3%	32.6%	82.3%	37.0%
Mean Pixel Diff(MPD)	\	3.0	19.4	2.7	23.3	2.8	19.2
analyze:

Official model has a good performence on lower PPR
Sigmoid function can not bring a good convergence result
Linear function shows a better convergence performence
It has a strong relation between dataset and test results. CTW is a hard sample with much shelter and difficult to recognize even for a human.

More Resource

Datatang (Beijing) Technology Co., Ltd is a professional AI data service provider and is dedicated to providing data collection, processing and data product services for global artificial intelligence enterprises, covering data types such as speech, image and text, including biometrics, speech recognition, autonomous driving, smart home, smart manufacturing, new retail, OCR scene, smart medical treatment, smart transportation, smart security, mobile phone entertainment, etc.

For more open source data sets

For more business data sets

jasper-bai / datatang_craft-based_chinese_detector_training Goto Github PK

datatang_craft-based_chinese_detector_training's Introduction