absir-arabic-ocr-1.6B

Page-level OCR for printed Arabic documents. A fine-tune of LiquidAI/LFM2.5-VL-1.6B that reads a full page image and returns its text in reading order, with light HTML structure:

• <p> paragraphs, <h1>…<h6> headings
• <strong>, <sup> (note markers), <br>, <hr>
• <table> for printed tables
• the footnote block wrapped in <footnotes>

Diacritics (tashkeel) are kept as printed.

Usage

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

repo = "g3ida/absir-arabic-ocr-1.6B"
processor = AutoProcessor.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16).to("cuda").eval()

prompt = ("Transcribe this page. Return the text with its original HTML "
          "structure. Put the footnote block inside <footnotes>.")
messages = [{"role": "user", "content": [
    {"type": "image", "image": Image.open("page.png").convert("RGB")},
    {"type": "text", "text": prompt}]}]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
                                       return_dict=True, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=2560, do_sample=False, repetition_penalty=1.05)
print(processor.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Recommended decoding: greedy, repetition_penalty=1.05, max_new_tokens=2560. The penalty matters for this model: it removes most repetition loops at a small cost in median CER. Render PDF pages at ~170 DPI.

Evaluation

All three benchmarks are public and none were used for training. Other models' numbers are copied from the benchmark papers; ours were measured with the same data and scoring code where it is published.

KITAB-Bench — OCR, hindawi subset (200 book pages)

Hindawi is the only full-page printed-document subset of KITAB-Bench's OCR task, and we use all 200 of its pages. The other subsets are handwriting, scene text, slides, or synthetic single lines and words, which this page-level model was not built for. Every row is a score on the same 200 hindawi pages. Other models' numbers are their hindawi results from the KITAB-Bench paper (Table 9), not their whole-benchmark totals. Scored with the official KITAB-Bench preprocessing and metrics. Lower is better.

Model	CER	WER
Gemini-2.0-Flash	0.01	0.04
absir-arabic-ocr-1.6B	0.046*	0.145*
Azure OCR	0.06	0.28
Qari	0.11	0.15
Surya	0.15	0.20
Qwen2.5-VL-7B	0.27	0.42
Tesseract	0.31	0.72
GPT-4o	0.34	0.56
GPT-4o-mini	0.48	0.71
PaddleOCR	0.76	1.00

* The model returns HTML (<p>, <h1>, <footnotes>, …), but the hindawi ground truth is plain text with no markup. We remove the tags from our output (re.sub(r"<[^>]+>", " ", pred)) before scoring, so only the transcribed text is compared. Everything else is KITAB-Bench's own pipeline, unchanged: its preprocess_arabic_text normaliser, then torchmetrics CharErrorRate / WordErrorRate over all 200 pages. Counting the tags as errors gives CER 0.073 / WER 0.167. Greedy decoding without repetition penalty.

KITAB-Bench — table recognition (241 HTML tables)

TEDS, higher is better. The published models were prompted with KITAB-Bench's own table prompt. Ours is scored with the instruction it was trained on (the prompt above); with KITAB's prompt it scores 46.8.

Model	TEDS
GPT-4o	85.8
Gemini-2.0-Flash	83.1
AIN-7B	75.9
GPT-4o-mini	69.3
absir-arabic-ocr-1.6B (trained prompt)	64.6
Qwen2.5-VL-7B	59.3
Qwen2-VL-7B	57.8
Surya	50.2
EasyOCR (Docling)	49.1
absir-arabic-ocr-1.6B (KITAB's prompt)	46.8
Tesseract (Docling)	28.2

The 64.6 row uses the model's own prompt (see Usage). The 46.8 row uses KITAB's table prompt. Greedy decoding with repetition_penalty=1.05.

Misraj-DocOCR (modern documents, out of domain)

Mean over pages, lower is better.

Model	WER	CER
Baseer	0.25	0.53
Gemini-2.5-pro	0.37	0.31
Azure AI Document Intelligence	0.44	0.27
Dots.ocr	0.50	0.40
absir-arabic-ocr-1.6B	0.69	0.58
Nanonets	0.71	0.55
Qwen2.5-VL-32B	0.76	0.59
Qari	0.76	0.64
GPT-5	0.86	0.62
Qwen2.5-VL-3B	0.87	0.71
Qwen2.5-VL-7B	0.92	0.77
GPT-4o-mini	1.36	1.10

All 400 pages. Not a strict like-for-like comparison: ours is scored with markup stripped and Arabic normalised (the paper does not publish its scoring code). repetition_penalty=1.05. The mean is driven by a few runaway pages: the median page has CER 0.23 / WER 0.36, and 11% of pages have CER above 1.

Sources: KITAB-Bench (arXiv:2502.14949, Tables 5 and 9), Baseer / Misraj-DocOCR (arXiv:2509.18174, Table 5).

Limitations

• Trained mostly on book pages. Modern documents, newspapers and dense multi-column layouts are weaker (see Misraj-DocOCR above), and hard pages can fall into repetition loops.
• The language model can occasionally replace a hard-to-read word with a fluent but different one. Treat output as a draft for critical use.

License

LFM Open License v1.0, inherited from the base model. See LICENSE.