captcha-crnn-base

from transformers import pipeline
from PIL import Image

# Initialize the pipeline
pipe = pipeline(
    task="captcha-recognition", 
    model="Graf-J/captcha-crnn-base", 
    trust_remote_code=True
)

# Load and predict
img = Image.open("path/to/image.png")
result = pipe(img)
print(f"Decoded Text: {result['prediction']}")

import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

# Load Model & Custom Processor
repo_id = "Graf-J/captcha-crnn-base"
processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)

model.eval()

# Load and process image
img = Image.open("path/to/image.png")
inputs = processor(img) 

# Inference
with torch.no_grad():
    outputs = model(inputs["pixel_values"])
    logits = outputs.logits

# Decode the prediction via CTC logic
prediction = processor.batch_decode(logits)[0]
print(f"Prediction: '{prediction}'")