shinTB
Abstract
A python package for use Textboxes : Image Text Detection Model
implemented by tensorflow, cv2
Textboxes Paper Review in Korean (My Blog) : shinjayne.github.io/textboxes
shintb
: useable textboxes python package (Source codes are in here)
svt1
: Street view Text dataset. can use with shintb.svt_data_loader.SVTDataLoader
when training Textboxes model
config.py
: (NECESSARY) configuration of model building and training with shinTB
main.py
: simple example useage of shinTB
package
Dependancies
- python Version: 3.5.3
- numpy Version: 1.13.0
- tensorflow Version: 1.2.1
- cv2
How to use
- Clone this repository to your local.
- You will use
shintb
python package andconfig.py
for building and training your own Textboxes model. svt1
gives us training / test data.- Open new python file.
- Import
config.config
andshintb
.
from config import config
from shintb import graph_drawer, default_box_control, svt_data_loader, runner
- Initialize
GraphDrawer
,DefaultBoxControl
,SVTDataLoader
instance.
graphdrawer = graph_drawer.GraphDrawer(config)
dataloader = svt_data_loader.SVTDataLoader('./svt1/train.xml', './svt1/test.xml')
dbcontrol = default_box_control.DefaultBoxControl(config, graphdrawer)
-
GraphDrawer
instance contains a tensorflow graph of Textboxes. -
DefaultboxControl
instance contains methods and attributes which is related to default box. -
SVTDataLoader
instance loads data fromsvt1
. -
Initialize
Runner
instance.
runner = runner.Runner(config, graphdrawer, dataloader, dbcontrol)
Runner
usesGraphDrawer
,DefaultBoxControl
,SVTDataLoader
instance.- If you want to train your Textboxes model, use
Runner.train()
. Every 1000 step,shintb
will save ckpt file in the directory you set inconfig.py
.
runner.train()
- If you want to validate/test your model, use
Runner.test()
runner.test()
- After training, if you want to detect texts from one image use
Runner.image()
.
runner.image(<your_image_directory>)