How to Crack Image Captchas with Python: Generation, Pre‑processing, and OCR
This tutorial walks through the four main captcha types, focuses on image captchas, explains generation with the Claptcha library, details preprocessing steps such as grayscale conversion, binarization, denoising, and character segmentation, and demonstrates recognition using tesserocr, while showing the impact of noise and interference lines.
When building web crawlers, captchas are an unavoidable obstacle; they generally fall into four categories: image, slide, click, and voice.
This article concentrates on image captchas, which typically consist of digit or letter combinations (sometimes Chinese characters) and are made harder by adding noise points, interference lines, distortions, overlapping, and varied font colors.
The typical recognition pipeline includes:
Grayscale conversion
Contrast enhancement (optional)
Binarization
Denoising
Skew correction and character segmentation
Building a training dataset
Recognition
For experimental purposes the captchas are generated programmatically, allowing large, labeled datasets. The Claptcha library (or the Captcha library) is used; to create the simplest numeric, interference‑free captchas the function _drawLine at line 285 of claptcha.py is modified to return None.
Generated examples:
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