blocks|key|694541|text|由于您的图像不涉及任何复杂的背景，所以我使用Otsu的方法为我选择正确的阈值：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|694542|threshold,+thresh_img+=+cv2.threshold(imgGray,+0,+255,+cv2.THRESH_BINARY_INV+%2B+cv2.THRESH_OTSU)|code-block|syntax|javascript|694543|​|694544|📷|atomic|offset|length|694545|694546|现在，当你找到等高线时，它会更容易。这是因为在白色的物体或字符中可以找到等高线。|694547|694548|694549|694550|看看那些现在引起问题的字符：|694551|694552|694553|694554|694555|694556|694557|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/790581689038e9d856571f56465ef46c.jpg|imageAlt|1|https://developer.qcloudimg.com/http-save/yehe-900000/9f4c85f0654b9290e448d16f14fd8a9a.jpg|2|https://developer.qcloudimg.com/http-save/yehe-900000/4f9c719bd2bec52f8dcf8be920474973.jpg|3|https://developer.qcloudimg.com/http-save/yehe-900000/0c5a62d723d43d4451e4387d27485662.jpg^0|0|0|0|0|1|0|0|0|0|0|0|1|1|0|0|0|0|0|1|2|0|0|0|0|1|3|0|0^^$0|@$1|2|3|4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1H|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1I|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1J|8|@]|9|@$L|1K|M|1L|1|1M]]|A|$]]|$1|N|3|H|5|6|7|1N|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1O|8|@]|9|@]|A|$]]|$1|Q|3|H|5|6|7|1P|8|@]|9|@]|A|$]]|$1|R|3|J|5|K|7|1Q|8|@]|9|@$L|1R|M|1S|1|1T]]|A|$]]|$1|S|3|H|5|6|7|1U|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|1V|8|@]|9|@]|A|$]]|$1|V|3|H|5|6|7|1W|8|@]|9|@]|A|$]]|$1|W|3|J|5|K|7|1X|8|@]|9|@$L|1Y|M|1Z|1|20]]|A|$]]|$1|X|3|H|5|6|7|21|8|@]|9|@]|A|$]]|$1|Y|3|H|5|6|7|22|8|@]|9|@]|A|$]]|$1|Z|3|J|5|K|7|23|8|@]|9|@$L|24|M|25|1|26]]|A|$]]|$1|10|3|H|5|6|7|27|8|@]|9|@]|A|$]]|$1|11|3|-4|5|6|7|28|8|@]|9|@]|A|$]]]|12|$13|$5|14|15|16|A|$17|18|19|-4]]|1A|$5|14|15|16|A|$17|1B|19|-4]]|1C|$5|14|15|16|A|$17|1D|19|-4]]|1E|$5|14|15|16|A|$17|1F|19|-4]]]]

Since your image does not involve any complex background I used Otsu's method to pick the right threshold for me:

<pre><code>threshold, thresh_img = cv2.threshold(imgGray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
</code></pre>

<a href="https://i.stack.imgur.com/aecax.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/aecax.jpg" alt="enter image description here"></a>

Now when you find contours it will be easier. That is because contours are found for objects or charaters that are in white.

<a href="https://i.stack.imgur.com/AJLF5.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/AJLF5.jpg" alt="enter image description here"></a>

Have a look at those characters that caused problems now:

<a href="https://i.stack.imgur.com/TQQtn.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TQQtn.jpg" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/CtEAo.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CtEAo.jpg" alt="enter image description here"></a>

I'm new to image processing. I want to perform character segmentation in OCR. I have already done necessary pre-processing. When I perform character segmentation by finding contouring it works well except for the character 3, 8.

After pre processed image looks like this,

<a href="https://i.stack.imgur.com/SyBkb.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SyBkb.jpg" alt="pre processed image"></a>

Output after finding contouring for 3 and 8 is

<a href="https://i.stack.imgur.com/dJaxV.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/dJaxV.jpg" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/00xia.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/00xia.jpg" alt="enter image description here"></a>

Code used:

<pre><code>imgGray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ret, imgThresh = cv2.threshold(imgGray, 127, 255, 0)
image, contours , _ = cv2.findContours(imgThresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
</code></pre>

But it gives good result for other characters:

<a href="https://i.stack.imgur.com/dN51B.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/dN51B.jpg" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/v8yB8.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/v8yB8.jpg" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/zC7G2.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/zC7G2.jpg" alt="enter image description here"></a>

How to solve this issue?

Character segmentation not working for 3,8

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我对图像处理很陌生。我想在OCR中执行字符分割。我已经做过必要的预处理了。当我通过寻找轮廓来执行字符分割时，除了字符3，8之外，它工作得很好。经过预处理后的图像如下，​​求出3和8的轮廓后的输出是​​​​所用代码：imgGray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)ret, imgThresh = cv2.threshold(imgGray, 127, 2

3,8字符切分不起作用-腾讯云开发者社区-腾讯云

问3,8字符切分不起作用
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