How to Crack Image Captchas with Python: Grayscale, Binarization, and Tesserocr

When building web crawlers, captchas are an unavoidable obstacle; they can be roughly divided into four categories: image, slide, click, and audio.

We start with the image type, which usually consists of digits, letters, or even Chinese characters, often augmented with noise points, interference lines, distortion, overlapping, and varied font colors to increase difficulty.

Grayscale conversion

Contrast enhancement (optional)

Binarization

Denoising

Skew correction and character segmentation

Training set creation

Recognition

For experimentation we generate captchas using the Claptcha library (the Captcha library is also a good alternative). To create the simplest pure‑numeric, noise‑free captchas we modify the _drawLine function at line 285 of claptcha.py to return None, then generate the images.

These captchas show slight deformation. For such simple cases we can directly use Google’s open‑source tesserocr for recognition.

Installation:

Recognition:

The simple captcha yields a high recognition rate without additional processing.

Next we add noise points to the background and observe the results.

Recognition remains acceptable.

We then generate an alphanumeric captcha (including confusing characters like lower‑case “o”, upper‑case “O”, digit “0”, lower‑case “z”, upper‑case “Z”, and digit “2”).

Human eyes struggle with these, and the OCR also fails when interference lines are added.

We restore the original _drawLine function to add interference lines and see the impact.

With a line added, recognition drops to zero, prompting a need for line removal.

Since the image is effectively grayscale, we first convert it to a single‑channel intensity image.

After processing, the image becomes much sharper.

We attempt to remove interference lines using 4‑neighbour or 8‑neighbour algorithms: a pixel is considered noise if the count of neighboring pixels with value 255 exceeds a configurable threshold.

After denoising, the image looks sharper, but when the line width matches the digit strokes, the algorithm fails to remove it.

For captchas where line and digit widths differ (e.g., those generated by the Captcha library), the denoising works well.

Multiple passes of denoising further improve the result.

Recognition on the cleaned image yields accurate results.

In summary, this first article records how to perform grayscale conversion, binarization, and denoising on captcha images and recognize simple captchas using tesserocr; further techniques will be covered in the next installment.