描边宽度变换 (SWT) 实现 (Python) [英] Stroke Width Transform (SWT) implementation (Python)

问题描述

谁能描述我如何使用 opencv 或 simplecv 在 python 中实现 SWT?

Can anyone describe how can i implement SWT in python using opencv or simplecv ?

推荐答案

好的，这里是:

包含实现细节的链接，底部有代码下载链接:SWT

The link that has details on the implementation with the code download link at the bottom: SWT

为了完整起见，还要提到 SWT 或 Stroke Width Transform 是 Epshtein 等人在 2010 年设计的，并且已经证明是迄今为止最成功的文本检测方法之一.它不使用机器学习或精心设计的测试.基本上在输入图像上进行 Canny 边缘检测之后，它会计算构成图像中对象的每个笔划的粗细.由于文本具有一致的粗笔画，这可能是一个强大的识别特征.

For the sake of completeness, also mentioning that SWT or Stroke Width Transform was devised by Epshtein and others in 2010 and has turned out to be one of the most successful text detection methods til date. It does not use machine learning or elaborate tests. Basically after Canny edge detection on the input image, it calculates the thickness of each stroke that makes up objects in the image. As text has uniformly thick strokes, this can be a robust identifying feature.

链接中给出的实现是使用 C++、OpenCV 和 Boost 库，它们用于连接图遍历等.计算 SWT 步长.我个人已经在 Ubuntu 上对其进行了测试，它运行得很好(而且效率很高)，但准确性并不准确.

The implementation given in the link is using C++, OpenCV and the Boost library they use for the connected graph traversals etc. after the SWT step is computed. Personally I've tested it on Ubuntu and it works quite well (and efficiently), though the accuracy is not exact.

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