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[验证码识别-部署] This project is based on CNN+BLSTM+CTC to realize verificationtion. This projeccode identificat is only for deployment models.
| Date | Stars |
|---|---|
| 2026-07-24 | 681 |
| 2026-07-25 | 681 |
| 2026-07-28 | 681 |
| 2026-07-30 | 681 |
| 2026-08-06 | 681 |
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growth rate 0.00%/day
<a href="https://github.com/kerlomz/captcha_platform/blob/master/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg"></a>
[](https://travis-ci.org/kerlomz/captcha_platform)
# Project Introduction
This project is based on CNN+BLSTM+CTC to realize verification code identification.
This project is only for deployment models, If you need to train the model, please move to https://github.com/kerlomz/captcha_trainer
# Informed
1. The default requirements.txt will install CPU version, Change "requirements.txt" from "TensorFlow" to "TensorFlow-GPU" to Switch to GPU version, Use the GPU version to install the corresponding CUDA and cuDNN.
2. demo.py: An example of how to call a prediction method.
3. The model folder folder is used to store model configuration files such as model.yaml.
4. The graph folder is used to store compiled models such as model.pb
5. The deployment service will automatically load all the models in the model configuration. When a new model configuration is added, the corresponding compilation model in the graph folder will be automatically loaded, so if you need to add it, please copy the corresponding compilation model to the graph path first, then add the model configuration.
# Start
1. Install the python 3.9 environment (with pip)
2. Install virtualenv ```pip3 install virtualenv```
3. Create a separate virtual environment for the project:
```bash
virtualenv -p /usr/bin/python3 venv # venv is the name of the virtual environment.
cd venv/ # venv is the name of the virtual environment.
source bin/activate # to activate the current virtual environment.
cd captcha_platform # captcha_platform is the project path.
```
4. ```pip install -r requirements.txt```
5. Place your trained model.yaml in model folder, and your model.pb in graph folder (create if not exist)
6. Deploy as follows.
## 1. Http Version
1. Linux
Deploy (Linux/Mac):
Port: 19952
```
python tornado_server.py
```
2. Windows
Deploy (Windows):
```
python xxx_server.py
```
3. Request
|Request URI | Content-Type | Payload Type | Method |
| ----------- | ---------------- | -------- | -------- |
| http://localhost:[Bind-port]/captcha/v1 | application/json | JSON | POST |
| Parameter | Required | Type | Description |
| ---------- | ---- | ------ | ------------------------ |
| image | Yes | String | Base64 encoding binary stream |
| model_name | No | String | ModelName, bindable in yaml configuration |
The request is in JSON format, like: {"image": "base64 encoded image binary stream"}
4. Response
| Parameter Name | Type | Description |
| ------- | ------ | ------------------ |
| message | String | Identify results or error messages |
| code | String | Status Code |
| success | String | Whether to request success |
The return is in JSON format, like: {"message": "xxxx", "code": 0, "success": true}
## 2. G-RPC Version
Deploy:
```
python3 grpc_server.py
```
Port: 50054
# Update G-RPC-CODE
python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. ./grpc.proto
# Directory Structure
- captcha_platform
- grpc_server.py
- flask_server.py
- tornado_server.py
- sanic_server.py
- demo.py
- config.yaml
- model
- model-1.yaml
- model-2.yaml
- ...
- graph
- Model-1.pb
- ...
# Management Model
1. **Load a model**
- Put the trained pb model in the graph folder.
- Put the trained yaml model configuration file in the model folder.
2. **Unload a model**
- Delete the corresponding yaml configuration file in the model folder.
- Delete the corresponding pb model file in the graph folder.
3. **Update a model**
- Put the trained pb model in the graph folder.
- Put the yaml configuration file with "Version" greater than the currenExcerpt of 4,337 characters
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The University of Texas at Austin · United States
169
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:34ee7ae4988b02c6, topic:ocr
matched fp:34ee7ae4988b02c6, topic:tensorflow