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State-of-the-art OCR model for historical Arabic manuscripts achieving 5.10% CER through native-resolution encoding, Arabic-native tokenization, and synthetic pretraining.
Source du modèle
Description de la source
State-of-the-art OCR model for historical Arabic manuscripts achieving 5.10% CER through native-resolution encoding, Arabic-native tokenization, and synthetic pretraining.
Sources
1 sourceVérifié 10 sept.
Artefacts du modèle
2 artefactsExtraits de sources
2 extraits| Dataset | CER | WER | Relative Improvement |
|---|---|---|---|
| MUHARAF | 8.35% | 24.76% | -71% vs TrOCR |
| KHATT | 11.21% | 37.36% | -37% vs TrOCR |
| RASAM | 4.95% | 18.94% | -86% vs TrOCR |
| Combined | 5.10% | 18.05% | -57% vs TrOCR |
State-of-the-Art: 36% relative improvement over previous best (HATFormer, 8% CER)
pip install transformers pillow torch
from transformers import AutoModel, AutoTokenizer
from PIL import Image
# Load model and tokenizer
model = AutoModel.from_pretrained("mdnaseif/hafith")
tokenizer = AutoTokenizer.from_pretrained("mdnaseif/hafith")
# Load manuscript image
image = Image.open("manuscript_line.jpg")
# Run OCR
with torch.no_grad():
outputs = model.generate(image, max_length=64)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Recognized text: {text}")
from datasets import load_dataset
# Load your manuscript dataset
dataset = load_dataset("your_dataset")
# Process in batches
batch_size = 32
for i in range(0, len(dataset), batch_size):
batch = dataset[i:i+batch_size]
images = [img.convert('RGB') for img in batch['image']]
outputs = model.generate(images, max_length=64)
texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
for img_id, text in zip(batch['id'], texts):
print(f"{img_id}: {text}")
Input Image (H×W×3)
↓
SigLIP V2 NaFlex Encoder
- 400M parameters
- Up to 512 patches (aspect-ratio preserving)
- Output: 512×1152 embeddings
↓
Projection Layer (1152 → 1024)
↓
RoBERTa-Large Decoder
- 24 layers, 16 attention heads
- Trained from scratch with Aranizer
- Cross-attention to visual features
↓
Aranizer Tokenizer (64K vocab)
↓
Arabic Text Output
| Model | Encoder | Tokenizer | CER | WER |
|---|---|---|---|---|
| CRNN+CTC | CNN | Character-level | 14.82% | - |
| TrOCR-Base | BEiT-B (384×384) | RoBERTa | 13.41% | - |
| TrOCR-Large | BEiT-L (384×384) | RoBERTa | 11.73% | 31.82% |
| HATFormer | BEiT-L (384×384) | RoBERTa | 8.60% | - |
| HAFITH (Ours) | SigLIP2 NaFlex | Aranizer | 5.10% | 18.05% |
@article{naseif2026hafith,
title={HAFITH: Aspect-Ratio Preserving Vision-Language Model for Historical Arabic Manuscript Recognition},
author={Naseif, Mohammed and Mesabah, Islam and Hajjaj, Dalia and Hassan, Abdulrahman and Elhayek, Ahmed and Koubaa, Anis},
journal={arXiv preprint arXiv:XXXX.XXXXX},
year={2026}
}
Apache 2.0
--- language: - ar license: apache-2.0 tags: - optical-character-recognition - historical-manuscripts - arabic - vision-language - document-ai datasets: - mdnaseif/hafith-combined-benchmark - mdnaseif/hafith-synthetic-1m metrics: - character-error-rate - word-error-rate library_name: transformers pipeline_tag: image-to-text --- # HAFITH: Aspect-Ratio Preserving Vision-Language Model for Historical Arabic Manuscript Recognition State-of-the-art OCR model for historical Arabic manuscripts achieving **5.10% CER** through native-resolution encoding, Arabic-native tokenization, and synthetic pretraining. ## Model Summary - **Architecture**: Vision-Language (Encoder-Decoder) - **Vision Encoder**: SigLIP V2 NaFlex (400M params, preserves aspect ratios up to 20:1) - **Text Decoder**: RoBERTa-Large (242M params, trained from scratch) - **Tokenizer**: Aranizer-PBE-64k (64K Arabic vocabulary) - **Total Parameters**: 642M - **Training**: 10 days on single RTX 4090 - **Inference Speed**: 12.5 samples/second (~45K lines/hour) ## Performance | Dataset | CER | WER | Relative Improvement | |---------|-----|-----|---------------------| | MUHARAF | 8.35% | 24.76% | -71% vs TrOCR | | KHATT | 11.21% | 37.36% | -37% vs TrOCR | | RASAM | 4.95% | 18.94% | -86% vs TrOCR | | **Combined** | **5.10%** | **18.05%** | **-57% vs TrOCR** | **State-of-the-Art**: 36% relative improvement over previous best (HATFormer, 8% CER) ## Key Innovations 1. **Native-Resolution Encoding**: Preserves aspect ratios (5:1 to 20:1) using SigLIP V2 NaFlex with variable patch counts 2. **Arabic-Native Tokenization**: Aranizer achieves 4:1 compression over character-level approaches 3. **Synthetic Pretraining**: 1M manuscript-style samples across 350 Arabic fonts for from-scratch decoder training ## Usage ### Installation ```bash pip install transformers pillow torch ``` ### Basic Inference ```python from transformers import AutoModel, AutoTokenizer from PIL import Image # Load model and tokenizer model = AutoModel.from_pretrained("mdnaseif/hafith") tokenizer = AutoTokenizer.from_pretrained("mdnaseif/hafith") # Load manuscript image image = Image.open("manuscript_line.jpg") # Run OCR with torch.no_grad(): outputs = model.generate(image, max_length=64) text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Recognized text: {text}") ``` ### Batch Processing ```python from...
Source context: 57 downloads · 0 likes · Pipeline image-to-text · Library transformers · Repo mdnaseif/hafith