CER (Character Error Rate): 0.02025931928687196
Hyperparameters:
- Learning Rate: 1.1081459294764632e-05
- Batch Size: 4
- Num Epochs: 3
- Warmup Ratio: 0.07863134774153628
- Weight Decay: 0.06248152825021373
- Num Beams: 6
- Length Penalty: 0.5095100725173662

Usage

python

from transformers import VisionEncoderDecoderModel, TrOCRProcessor
import torch
from PIL import Image

# Load model and processor
processor = TrOCRProcessor.from_pretrained("anuashok/ocr-captcha-v2")
model = VisionEncoderDecoderModel.from_pretrained("anuashok/ocr-captcha-v2")

# Load image
image = Image.open('path_to_your_image.jpg').convert("RGB")

# Prepare image
pixel_values = processor(image, return_tensors="pt").pixel_values

# Generate text
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)