螺旋菜鸟 发表于 2021-8-10 16:47:14

求大佬赐教,如何识别这种验证码

本帖最后由 螺旋菜鸟 于 2021-8-10 16:49 编辑

最近在做自动化,遇到验证码,想自己用pytesseract来识别些简单的图形验证码。
请问以下这种图片如何识别data:image/png;base64,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
是否需要使用二值化、灰度处理?




jy2021 发表于 2021-8-21 08:33:17

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