Captcha Solver Python Github [ EASY → ]

Stars: 500+ | Status: Active

This is a thin but powerful wrapper around the Anti-Captcha API. It’s not a solver itself but connects to one of the most reliable human-powered services.

Key Features:

Quick Code Snippet:

from python_anticaptcha import AnticaptchaClient, ImageToTextTask
client = AnticaptchaClient("YOUR_API_KEY")
task = ImageToTextTask("captcha_image.png")
job = client.createTask(task)
job.join()
print(job.get_solution())

| Repository | Stars | Best For | |------------|-------|----------| | captcha-breaker | 500+ | Simple text CAPTCHAs | | CNN-captcha-solver | 300+ | Learning deep learning approach | | Captcha-solving-tensorflow | 200+ | Production-ready training |

python train.py --epochs 50 --batch-size 32

FROM python:3.9-slim
RUN apt-get update && apt-get install -y 

tesseract-ocr 

libgl1-mesa-glx 

&& rm -rf /var/lib/apt/lists/* captcha solver python github
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "solver.py"]

# Install: pip install pytesseract pillow opencv-python
import pytesseract
import cv2
import numpy as np
from PIL import Image
def solve_simple_captcha(image_path):
# Load and preprocess
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Noise removal and thresholding
denoised = cv2.medianBlur(gray, 3)
_, thresh = cv2.threshold(denoised, 127, 255, cv2.THRESH_BINARY_INV)
# OCR
text = pytesseract.image_to_string(thresh, config='--psm 8')
return text.strip()

python predict.py --image test_captcha.png
 Stars: 500+ | Status: Active This is a

# Clone and follow: https://github.com/JackonYang/captcha-tensorflow
def create_captcha_model(char_count=4, img_size=(100, 40)):
model = keras.Sequential([
keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(*img_size, 1)),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Conv2D(64, (3,3), activation='relu'),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Flatten(),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(char_count * 36, activation='softmax')  # 36 chars (a-z0-9)
])
return model