captcha-solver-ai 0.1.1

Neural net CAPTCHA solver. MobileNetV2 + OpenCV grid splitting. CLI and Python API. Built for the hell of it.

captcha-solver-ai

Neural net CAPTCHA solver. MobileNetV2 + OpenCV. Built for the hell of it.

Takes a reCAPTCHA image grid, splits it into cells, classifies each cell with a MobileNetV2 ImageNet classifier, and tells you which ones match the prompt. Also works live on Playwright pages.

Install

pip install captcha-solver-ai

With Playwright support (for solving CAPTCHAs on live pages):

pip install captcha-solver-ai[browser]
playwright install chromium

CLI

# Solve a CAPTCHA grid image
captcha-solver solve captcha.png --prompt "traffic lights"
captcha-solver solve captcha.png --prompt "buses" --grid 4 --verbose

# Classify any image
captcha-solver classify photo.png --top 10

# Pre-download the model (~13MB, auto-downloads on first use anyway)
captcha-solver download-model

Output:

Prompt: traffic lights
Grid:   3x3
Match:  [0, 3, 6]

[X] [ ] [ ]
[X] [ ] [ ]
[X] [ ] [ ]

Found 3 matching cell(s)

Python API

from captcha_solver import CaptchaSolver

solver = CaptchaSolver()

# From an image file
result = solver.solve_file("captcha.png", prompt="Select all images with traffic lights")
print(result.matching_cells)  # [0, 3, 6]
print(result.grid_display())

# From a numpy array (OpenCV)
import cv2
img = cv2.imread("captcha.png")
result = solver.solve(img, prompt="buses", grid_size=3)

# From raw bytes
with open("captcha.png", "rb") as f:
    result = solver.solve_bytes(f.read(), prompt="bicycles")

# Check result
if result.solved:
    print(f"Found matches at cells: {result.matching_cells}")

Lower-level API

from captcha_solver import split_grid, classify_cells, classify_image
import cv2

# Split a grid image into cells
img = cv2.imread("captcha.png")
cells = split_grid(img, grid_size=3)  # returns 9 cell images

# Classify cells against a prompt
results = classify_cells(cells, prompt="traffic lights")
for r in results:
    print(f"Cell {r['index']}: match={r['match']}, confidence={r['target_max_prob']:.1%}")

# Classify a single image (raw ImageNet predictions)
preds = classify_image(img, top_k=5)
for class_idx, prob in preds:
    print(f"Class {class_idx}: {prob:.1%}")

Playwright (live CAPTCHA solving)

from playwright.async_api import async_playwright
from captcha_solver import CaptchaSolver

solver = CaptchaSolver()

async with async_playwright() as pw:
    browser = await pw.chromium.launch(headless=True)
    page = await browser.new_page()
    await page.goto("https://some-page-with-recaptcha.com")

    solved = await solver.solve_on_page(page)
    if solved:
        print("CAPTCHA solved!")

The solve_on_page method handles the full flow: clicks the checkbox with human-like mouse movement, and if Google serves an image challenge it screenshots the grid, classifies each cell, clicks the matches, and hits verify.

Supported CAPTCHA categories

22 categories mapped to ImageNet classes:

traffic lights, buses, bicycles, motorcycles, cars, taxis, bridges, boats, ships, airplanes, trains, trucks, fire hydrants, parking meters, stairs, mountains, palm trees, tractors

Will it solve every CAPTCHA? No. Google sometimes serves categories that don't map well to ImageNet, and image challenges can require multiple rounds. But it handles the common ones.

How it works

1. MobileNetV2 (pre-trained on ImageNet, 1000 classes) runs as an ONNX model (~13MB)
2. OpenCV splits the CAPTCHA grid into individual cells
3. Each cell is resized to 224x224, normalized, and fed through the network
4. Top-10 predictions are checked against a mapping of CAPTCHA keywords to ImageNet class indices
5. Cells where a target class appears in the top-10 or exceeds 5% probability are marked as matches

The model auto-downloads on first use and is cached at ~/.captcha_solver/.

License

MIT