---
license: apache-2.0
language:
- zh
- en
pipeline_tag: image-to-text
tags:
- ocr
- captcha
---



## 介绍（Introduction）
**验证码识别模型（ocr-captcha）**专门识别常见验证码的模型，训练模型有2个：
  
  1.**small**:训练数据大小为700MB，约8.4万张验证码图片，训练轮次27轮，最终的精度将近100%，推荐下载这个模型；
  
  2.**big**:训练数据大小为11G，约135万个验证码图片，训练轮次1轮，最终的精度将近93.95%(由于资源问题，无法训练太久)；

## 数据分布

  1.**类型**：1. 纯数字型；2. 数字+字母型；3.纯字母型（大小写）
  
  2.**长度**：4位、5位、6位

## 数据微调

  1.**基座模型**：基座模型参考达摩院发布的[读光-文字识别-行识别模型-中英-通用领域](https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo/summary)
  
  2.**具体微调参考以上链接**

## 模型体验链接

huggingface：[验证码识别模型（ocr-captcha）](https://huggingface.co/spaces/xiaolv/ocr)




## 快速使用（Quickstart）

代码提供web网页版：```myself_train_model.py```

```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import gradio as gr
import os


class xiaolv_ocr_model():

    def __init__(self):
        model_small = r"./output_small"
        model_big = r"./output_big"
        self.ocr_recognition_small = pipeline(Tasks.ocr_recognition, model=model_small)
        self.ocr_recognition1_big = pipeline(Tasks.ocr_recognition, model=model_big)


    def run(self,pict_path,moshi = "small", context=[]):
        pict_path = pict_path.name
        context = [pict_path]

        if moshi == "small":
            result = self.ocr_recognition_small(pict_path)
        else:
            result = self.ocr_recognition1_big(pict_path)

        context += [str(result['text'][0])]
        responses = [(u, b) for u, b in zip(context[::2], context[1::2])]
        print(f"识别的结果为：{result}")
        os.remove(pict_path)
        return responses,context




if __name__ == "__main__":
    pict_path = r"C:\Users\admin\Desktop\图片识别测试\企业微信截图_16895911221007.png"
    ocr_model = xiaolv_ocr_model()
    # ocr_model.run(pict_path)
```