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This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 2.3.3 instead.
Reason given by maintainers: bug：打包时忘记把 ppocr 模型相关的字符集文件打包进来了 😭

English README (out-dated).

cnocr

cnocr 是 Python 3 下的文字识别（Optical Character Recognition，简称OCR）工具包，支持中文、英文的常见字符识别，自带了多个训练好的识别模型，安装后即可直接使用。欢迎扫码加入微信交流群：

微信群二维码

作者也维护 知识星球 CnOCR/CnSTD私享群 ，欢迎加入。知识星球私享群会陆续发布一些CnOCR/CnSTD相关的私有资料，包括更详细的训练教程，未公开的模型，使用过程中遇到的难题解答等。本群也会发布OCR/STD相关的最新研究资料。此外，私享群中作者每月提供两次免费特有数据的训练服务。

详细文档

见 CnOcr在线文档 。

使用场景说明

cnocr 主要针对的是排版简单的印刷体文字图片，如截图图片，扫描件等。目前内置的文字检测和分行模块无法处理复杂的文字排版定位。如果要用于场景文字图片的识别，需要结合其他的场景文字检测引擎使用，例如文字检测引擎 cnstd 。

示例

图片 OCR结果
docs/examples/helloworld.jpg Hello world!你好世界
docs/examples/chn-00199989.jpg 铑泡胭释邑疫反隽寥缔
docs/examples/chn-00199980.jpg 拇箬遭才柄腾戮胖惬炫
docs/examples/chn-00199984.jpg 寿猿嗅髓孢刀谎弓供捣
docs/examples/chn-00199985.jpg 马靼蘑熨距额猬要藕萼
docs/examples/chn-00199981.jpg 掉江悟厉励.谌查门蠕坑
docs/examples/00199975.jpg nd-chips fructed ast
docs/examples/00199978.jpg zouna unpayably Raqu
docs/examples/00199979.jpg ape fissioning Senat
docs/examples/00199971.jpg ling oughtlins near
docs/examples/multi-line_cn1.png 网络支付并无本质的区别，因为
每一个手机号码和邮件地址背后
都会对应着一个账户--这个账
户可以是信用卡账户、借记卡账
户，也包括邮局汇款、手机代
收、电话代收、预付费卡和点卡
等多种形式。
docs/examples/multi-line_cn2.png 当然，在媒介越来越多的情形下,
意味着传播方式的变化。过去主流
的是大众传播,现在互动性和定制
性带来了新的挑战——如何让品牌
与消费者更加互动。
docs/examples/multi-line_en_white.png This chapter is currently only available in this web version. ebook and print will follow.
Convolutional neural networks learn abstract features and concepts from raw image pixels. Feature
Visualization visualizes the learned features by activation maximization. Network Dissection labels
neural network units (e.g. channels) with human concepts.
docs/examples/multi-line_en_black.png transforms the image many times. First, the image goes through many convolutional layers. In those
convolutional layers, the network learns new and increasingly complex features in its layers. Then the
transformed image information goes through the fully connected layers and turns into a classification
or prediction.

安装

嗯，安装真的很简单。

pip install cnocr

安装速度慢的话，可以指定国内的安装源，如使用豆瓣源：

pip install cnocr -i https://pypi.doubanio.com/simple

注意：请使用 Python3（3.6以及之后版本应该都行），没测过Python2下是否ok。

未来工作

  • 支持图片包含多行文字 (Done)
  • crnn模型支持可变长预测，提升灵活性 (since V1.0.0)
  • 完善测试用例 (Doing)
  • 修bugs（目前代码还比较凌乱。。） (Doing)
  • 支持空格识别（since V1.1.0）
  • 尝试新模型，如 DenseNet，进一步提升识别准确率（since V1.1.0）
  • 优化训练集，去掉不合理的样本；在此基础上，重新训练各个模型
  • 由 MXNet 改为 PyTorch 架构（since V2.0.0）
  • 基于 PyTorch 训练更高效的模型
  • 支持列格式的文字识别

Project links

Key dates

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  • Released:
  • Yanked:
Newer release available (2.3.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for breezedeus from gravatar.com breezedeus

Credits

Author: breezedeus

License

Apache Software License (Apache 2.0)

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