Azra-1-Mini

Azra 1-Mini 0.8b is a lightweight, high-performance Vision-Language OCR model specialized in Ottoman Turkish text transcription across both Nesih (printed/calligraphic) and Rika/Riqa (handwritten) scripts.

Despite having only 0.8 billion parameters, Azra 1-Mini achieves state-of-the-art accuracy on Ottoman Turkish OCR tasks, outperforming significantly larger proprietary models.

⚠️ Important Note on Input Resolution & Segmentation: This model has been fine-tuned and optimized specifically for line-level text images (satır bazlı görüntüler). It may not achieve optimal accuracy directly on full-page images without prior text line cropping/segmentation.

📊 Benchmark Results & Performance Comparison

The model was evaluated against leading proprietary Vision-Language models on standard Ottoman Turkish test sets using character accuracy (100% - CER).

1. Nesih Script Test Set (Printed / Calligraphic)

2. Rika Script Test Set (Handwritten)

🌟 Key Highlight: Azra 1-Mini 0.8b achieves 1st place on the handwritten Rika dataset (67.08%), significantly outperforming both Gemini 3.1 Pro and Qwen 3.8 Max while running efficiently at sub-billion parameter scale.

📷 Qualitative Results & Sample Transcriptions

Below are top qualitative predictions generated by Azra 1-Mini 0.8b from the evaluation test sets:

1. Nesih Script Samples (Printed / Calligraphic)

2. Rika Script Samples (Handwritten)

🚀 Usage Guide (transformers)

Below is the standard, native PyTorch & Hugging Face transformers implementation using AutoProcessor and Qwen3_5ForConditionalGeneration:

import os
import torch
from PIL import Image
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
from qwen_vl_utils import process_vision_info

# Device & dtype settings
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

model_id = "OttomanNLP/Azra-1-Mini-0.8b"

print("[INFO] Loading model and processor...")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=dtype,
    device_map="auto" if device == "cuda" else None,
    trust_remote_code=True
)
model.eval()
print("[INFO] Model loaded successfully!")

def extract_text(image_path: str, prompt: str = "Görseldeki Osmanlıca metni transkribe et:") -> str:
    """Extract Ottoman text from a line image"""
    if not os.path.exists(image_path):
        return f"File not found: {image_path}"

    image = Image.open(image_path).convert("RGB")

    # Adjust dimensions to multiples of 64
    w, h = image.size
    new_w = ((w + 63) // 64) * 64
    new_h = ((h + 63) // 64) * 64
    if (new_w, new_h) != (w, h):
        image = image.resize((new_w, new_h), Image.Resampling.LANCZOS)

    messages = [{
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": prompt}
        ]
    }]

    text_input = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, _ = process_vision_info(messages)

    inputs = processor(
        text=[text_input],
        images=image_inputs,
        padding=True,
        return_tensors="pt"
    ).to(device)

    with torch.inference_mode():
        generated_ids = model.generate(
            **inputs,
            max_new_tokens=512,
            do_sample=False,
            repetition_penalty=1.2,
            no_repeat_ngram_size=3,
            pad_token_id=processor.tokenizer.pad_token_id,
            eos_token_id=processor.tokenizer.eos_token_id,
        )

    input_len = inputs.input_ids.shape[1]
    output_text = processor.batch_decode(
        generated_ids[:, inpu