本文介绍了白色文本和黑色文本的单独图像。的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧! 问题描述 29岁程序员,3月因学历无情被辞!Hello everyone,I am a student currently working in a image processing project.My task is to separate white text and black text images.<a href="https://drive.google.com/open?id=0B81c7NmWqSF9UGFYTW0wRUNQM2c">3.jpg - Google Drive</a>[<a href="https://drive.google.com/open?id=0B81c7NmWqSF9UGFYTW0wRUNQM2c" target="_blank" title="New Window">^</a>]<a href="https://drive.google.com/open?id=0B81c7NmWqSF9eTFBQ0pYWGRfeUE">5.jpg - Google Drive</a>[<a href="https://drive.google.com/open?id=0B81c7NmWqSF9eTFBQ0pYWGRfeUE" target="_blank" title="New Window">^</a>]Can anyone suggest method/ parameter to differentiate these images.Thanks in advance. 我尝试了什么: 尝试了各种过滤器但由于图像背景不同而无法区分。What I have tried:Tried various filters but unable to differentiate as background is varying in images.推荐答案您只是概括了上一个问题。但我的答案已经足够普遍了:区分低对比度和高对比度文本图片。 我无法告诉您需要应用的精确图像转换/过滤器,因为它取决于您正在使用的平台和想象库。但是,视觉库的每一个非无意义的图像处理都有对比度操作的方法,以及处理噪声。此外,您可能需要将彩色图像转换为灰度图像(几乎不需要转换为黑白图像,但它取决于库,可用的blob识别,OCR等实现)。 我已经建议你创建一个实验应用程序,你可以在其中使用不同的转换/过滤器。获得一些经验并以交互方式找出真正有用的东西非常重要。这种方法非常有效。 -SAYou just generalized your previous question. But my answer was already general enough: Differentiating low and high contrast text images.I cannot tell you want exact image transformations/filters you have to apply, because it depends on the platform and imagine libraries you are using. But each and every non-nonsense image processing of vision library has the methods for contrast manipulations, as well as dealing with noise. Also, you may need to convert color images to gray-scale (converting to black and white is hardly needed, but it depends on the library, available implementations of blob recognition, OCR, and the like).I already advised you to create an experimental application where you can play with different transformations/filters. It is really important, to get some experience, and to figure out interactively what really works. This approach is really productive.—SA 这篇关于白色文本和黑色文本的单独图像。的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持! 上岸,阿里云! 09-02 05:35