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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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pipeline_tag: image-to-text |
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tags: |
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- ocr |
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- captcha |
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--- |
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## 介绍（Introduction） |
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**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： |
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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**； |
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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)； |
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## 数据分布 |
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1.**类型**：1. 纯数字型；2. 数字+字母型；3.纯字母型（大小写） |
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2.**长度**：4位、5位、6位 |
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## 数据微调 |
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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) |
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2.**具体微调参考以上链接** |
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## 模型体验链接 |
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modelscope：[验证码识别模型（ocr-captcha）](https://modelscope.cn/studios/xiaolv/ocr/summary) |
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## 单独模型链接（modelscope） |
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1.**[验证码识别模型（小）-small](https://modelscope.cn/models/xiaolv/ocr_small/summary)** |
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2.**[验证码识别模型（大）-big](https://modelscope.cn/models/xiaolv/ocr_big/summary)** |
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## 快速使用（Quickstart） |
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代码提供web网页版：```myself_train_model.py``` |
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```python |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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import gradio as gr |
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import os |
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class xiaolv_ocr_model(): |
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def __init__(self): |
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model_small = r"./output_small" |
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model_big = r"./output_big" |
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self.ocr_recognition_small = pipeline(Tasks.ocr_recognition, model=model_small) |
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self.ocr_recognition1_big = pipeline(Tasks.ocr_recognition, model=model_big) |
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def run(self,pict_path,moshi = "small", context=[]): |
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pict_path = pict_path.name |
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context = [pict_path] |
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if moshi == "small": |
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result = self.ocr_recognition_small(pict_path) |
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else: |
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result = self.ocr_recognition1_big(pict_path) |
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context += [str(result['text'][0])] |
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responses = [(u, b) for u, b in zip(context[::2], context[1::2])] |
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print(f"识别的结果为：{result}") |
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os.remove(pict_path) |
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return responses,context |
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if __name__ == "__main__": |
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pict_path = r"C:\Users\admin\Desktop\图片识别测试\企业微信截图_16895911221007.png" |
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ocr_model = xiaolv_ocr_model() |
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# ocr_model.run(pict_path) |
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``` |
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## 联系我们（Contact Us） |
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如果你想给我们的研发团队和产品团队留言，请通过邮件（2240560729@qq.com）联系我们。 |
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