This repository contains the official models and results for Ketaba-OCR, our submission to the NakbaNLP 2026 Shared Task (AR-MS) on Arabic Manuscript Understanding (Subtask 2: Systems Track). We rank 1st on per-line evaluation (CER 0.0819, WER 0.2588) and 3rd on the official (corpus-wide) leaderboard (CER 0.0938, WER 0.2996).

By: Hassan Barmandah, Fatimah Emad Eldin, Khloud Al Jallad, Omer Nacar — NAMAA Community (with Umm Al-Qura University, Trouve Labs, Syrian Society for Startups and Research, Tuwaiq Academy)

HuggingFace

Open In Colab

Model Description

This project introduces a parameter-efficient approach for Arabic handwritten text recognition (HTR) on historical manuscripts. The system is built upon Sherif's pretrained Arabic-English HTR model, which leverages prior training on diverse handwritten datasets including Kitab and IAM. Rather than training from scratch, we fine-tune the HTR backbone using Low-Rank Adaptation (LoRA) with 4-bit quantization (QLoRA), along with DoRA and RSLoRA for improved training stability.

A key element of this system is its ensemble strategy using a novel Linear+Boost weighted voting scheme. The task reports two evaluation schemes: corpus-wide (entire test set as one sequence; used for official ranking) and per-line (CER/WER averaged over examples). We achieve 1st place on per-line evaluation with CER 0.0819 and WER 0.2588, and 3rd place on the official (corpus-wide) leaderboard with CER 0.0938 and WER 0.2996.

The model transcribes cropped line images from Arabic manuscripts into machine-readable text, specifically optimized for the Omar Al-Saleh Memoir Collection (1951-1965) written in Ruq'ah and Naskh script variants.

Key Contributions

Ranking & Performance: 1st place on per-line evaluation (CER 0.082, WER 0.259) and 3rd place on the official corpus-wide leaderboard (CER 0.0938, WER 0.2996)
HTR vs. Generalist VLMs: Demonstrated that specialized fine-tuned HTR models drastically outperform zero-shot generalist VLMs
Parameter Efficiency: QLoRA efficiently bridged the domain gap, reducing CER from 0.58 to ~0.09 with minimal computational overhead (~8GB VRAM)
Ensemble Innovation: Linear+Boost weighting strategy improved CER by 7.4% over standard inverse-CER weighting (on per-line evaluation)

🚀 How to Use

You can use the fine-tuned model directly with the transformers and peft libraries. The following example demonstrates inference on a manuscript line image.

python

import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration, BitsAndBytesConfig
from peft import PeftModel
from PIL import Image
from qwen_vl_utils import process_vision_info

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load base model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "sherif1313/Arabic-English-handwritten-OCR-v3",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "HassanB4/Ketab-OCR-LoRA")

# Apply weight tying fix (critical for correct output)
model.lm_head.weight = model.model.language_model.embed_tokens.weight

# Load processor
processor = AutoProcessor.from_pretrained(
    "sherif1313/Arabic-English-handwritten-OCR-v3",
    trust_remote_code=True
)

# Example inference
image = Image.open("manuscript_line.png").convert("RGB")

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "اقرأ النص الموجود في الصورة:"}
    ]
}]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, _ = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=512, do_sample=False)

transcription = processor.decode(output_ids[0][len(inputs['input_ids'][0]):], skip_special_tokens=True)
print(transcription)

⚙️ Training Procedure

The system employs QLoRA fine-tuning of a specialized pretrained HTR model, rather than training a general-purpose VLM from scratch.

Training Data

The model was fine-tuned on the official NakbaNLP 2026 dataset from the Omar Al-Saleh Memoir Collection. We trained on the full available dataset (train + dev test combined):

Split	Samples	Description
Training (Used)	18,057	Train (15,962) + Dev Test (2,095) combined
Blind Test	2,671	Held-out for official CodaBench evaluation

Hyperparameters

Parameter	Value	Parameter	Value
Base Model	sherif1313/Arabic-English-OCR-v3	Architecture	Qwen2.5-VL-3B
Model Size	~4.07B parameters	Trainable Params	75.6M (1.97%)
Quantization	4-bit NF4 (QLoRA)	Compute Dtype	bfloat16
Double Quant	True	Pretraining Data	Kitab, IAM, Custom
LoRA Rank (r)	32	LoRA Alpha (α)	64
Target Modules	q, k, v, o, gate, up, down	LoRA Dropout	0.05
DoRA	True	RSLoRA	True
Learning Rate	2×10⁻⁵	Optimizer	AdamW (fused)
LR Scheduler	Cosine	Warmup Steps	200
Batch Size	1 (per GPU)	Gradient Accumulation	4
Effective Batch	4	Number of Epochs	1
Max Gradient Norm	1.0	Weight Decay	0.01
Max Sequence Length	2048	Max Image Size	1024

Ensemble Strategy

Our final submission employs a Linear+Boost weighted ensemble (Config 18) combining predictions from six model variants:

python

# Config 18: Linear+Boost<0.15
weights = normalize((1 - CER) + (CER < 0.15) * 0.5)

This applies a linear decay based on CER, plus a bonus weight of 0.5 for models with CER below 0.15 (rewarding the top 2 performers).

Models in Ensemble (6 variants):

Model Variant	CER	Gets Boost
Fine-tuned HRT (submission_1)	0.09	✓
Fine-tuned HRT (blind_inference)	0.11	✓
LoRA HRT (blind_hrt_lora)	0.18	—
Zero-shot HRT (blind_test)	0.20	—
Fine-tuned QARI (blind_qari)	0.26	—
Arabic OCR 4-bit v2	0.32	—

The ensemble algorithm uses:

Weighted Majority Voting: Predictions exceeding 50% weighted consensus are selected directly
Arabic Normalization: For disagreements, normalize alef variants and teh marbuta before voting
N-gram Consistency: Score predictions by 3-gram overlap with other models
Edit Distance Consensus: Final tie-breaking uses minimum average edit distance

Frameworks

PyTorch 2.5.0
Hugging Face Transformers ≥4.45.0
PEFT ≥0.14.0
bitsandbytes ≥0.43.0
Flash Attention 2.8.3

📊 Evaluation Results

The task reports two evaluation schemes: (1) Corpus-wide: entire test set as a single sequence (used for the official ranking); (2) Per-line: CER/WER computed per example then averaged. The primary metric is Character Error Rate (CER).

Our Results

Evaluation	Rank	CER	WER
Per-line	1st	0.0819	0.2588
Corpus-wide (official)	3rd	0.0938	0.2996

Final Test Set Scores (Per-Line)

System	Test CER	Test WER	Blind CER	Blind WER
Organizer Baseline	0.584	0.881	0.591	0.885
Zero-Shot HRT (Qwen2.5-VL)	0.169	0.499	0.203	0.503
Fine-Tuned HRT (Single Model)	0.081	0.115	0.088	0.270
Ketaba-OCR + Ensemble (Ours)	—	—	0.0819	0.2588

Comparison with Other Models

Model	Blind CER	Blind WER
Ketaba-OCR (Ours)	0.0819 (per-line)	0.2588 (per-line)
Fine-Tuned QARI-3	0.2635	0.5521
Arabic OCR 4-bit (Sherif)	0.3234	0.6203
Qwen2.5-VL-7B (Zero-Shot)	0.6808	0.9198
Qwen2.5-VL-3B (Zero-Shot)	0.6213	0.8628

⚠️ Limitations

Domain Specificity: Optimized for 1950s Ruq'ah/Naskh manuscripts; requires adaptation for other periods/styles
Agglutination Gap: WER (0.26–0.30) is disproportionately higher than CER (0.08–0.09) due to Arabic's agglutinative structure
Degraded Images: Performance degrades on severely faded or damaged manuscript regions
Generalization: Not tested on other historical Arabic manuscript collections

🙏 Acknowledgements

We thank the NakbaNLP 2026 organizers (Fadi Zaraket, Bilal Shalash, Hadi Hamoud, Ahmad Chamseddine, Firas Ben Abid, Mustafa Jarrar, Chadi Abou Chakra, Bernard Ghanem) for access to the Omar Al-Saleh Memoir Collection. We acknowledge Sherif for the pretrained Arabic-English OCR model, and the Hugging Face community for PEFT and bitsandbytes libraries.

📜 Citation

If you use this work, please cite the paper:

bibtex

@inproceedings{barmandah2026ketaba,
    title={{Ketaba-OCR at AR-MS NakbaNLP 2026: Efficient Adaptation of Vision-Language Models for Hand Written Recognition}},
    author={Barmandah, Hassan and Eldin, Fatimah Emad and Al Jallad, Khloud and Nacar, Omer},
    year={2026},
    booktitle={Proceedings of LREC 2026},
    note={NakbaNLP 2026 Shared Task}
}

📄 License

This project is licensed under the Apache 2.0 License.

HassanB4/Ketaba-OCR-LoRA

Ketaba-OCR at AR-MS NakbaNLP 2026: Efficient Adaptation of Vision-Language Models for Handwritten Text Recognition