text-detection-ctpn

GitHub describes it as text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network. The repository metadata lists Python as its primary language. The metadata lists the MIT license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network. The repository metadata lists Python as its primary language. The metadata lists the MIT license. The README describes the project this way: text detection mainly based on ctpn (connectionist text proposal network). It is implemented in tensorflow. I use id card detect as an example to demonstrate the results, but it should be noticing that this model can be used in almost every horizontal scene text detection task. The origin paper can be found here. Also, the origin repo in caffe can be found in here. For more detail about the paper and code, see this blog. If you got any questions, check the issue first, if the problem persists, open a new issue.

The README section "roadmap" states: - [x] freeze the graph for convenient inference - [x] pure python, cython nms and cuda nms - [x] loss function as referred in paper - [x] oriented text connector - [x] BLSTM

The README section "demo" states: - for a quick demo,you don't have to build the library, simpely use demo pb.py for inference. - first, git clone git@github.com:eragonruan/text-detection-ctpn.git --depth=1 - then, download the pb file from release - put ctpn.pb in data/ - put your images in data/demo, the results will be saved in data/results, and run demo in the root

The README section "parameters" states: there are some parameters you may need to modify according to your requirement, you can find them in ctpn/text.yml - USE GPU NMS whether to use nms implemented in cuda or not - DETECT MODE H represents horizontal mode, O represents oriented mode, default is H - checkpoints path the model I provided is in checkpoints/, if you train the model by yourself,it will be saved in output/

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README