---
license: other
language:
- ar
- ur
- fa
task_categories:
- image-to-text
tags:
- htr
- ocr
- arabic
- urdu
- manuscripts
- lithography
- historical
size_categories:
- 10K<n<100K
pretty_name: AMIDDA
source_datasets:
- calfa-ai/RASAM-1
- calfa-ai/RASAM-2
- calfa-ai/TARIMA
- calfa-ai/BAYBARS
- calfa-ai/ISKANDAR
- aamijar/muharaf-public
- johnlockejrr/KHATT_v1.0_dataset
---

# AMIDDA — A Dataset for Generic Arabic ATR model

AMIDDA (Arabic Manuscript Images for Digital Deciphering and Analysis) is a combined dataset aggregating line-level ground-truth from eight HTR datasets covering Arabic (Maghrebi & Oriental scripts), Urdu, Persian, and modern Arabic handwriting.

## Stats

| | |
|---|---|
| Sub-datasets | 8 |
| Lines (total) | 66,779 |
| Train / Val / Test | 53,420 / 6,675 / 6,684 |

## Sub-datasets

| Dataset | Script | Source | Lines |
|---|---|---|---|
| [RASAM-1](https://huggingface.co/datasets/calfa-ai/RASAM-1) | Arabic (Maghrebi) | BULAC | 7,331 |
| [RASAM-2](https://huggingface.co/datasets/calfa-ai/RASAM-2) | Arabic (Maghrebi) | BULAC | 3,720 |
| [TARIMA](https://huggingface.co/datasets/calfa-ai/TARIMA) | Arabic (Maghrebi) | BULAC / BnF / MMSH | 2,285 |
| [BAYBARS](https://huggingface.co/datasets/calfa-ai/BAYBARS) | Arabic (Oriental) | — | 15,567 |
| [ISKANDAR](https://huggingface.co/datasets/calfa-ai/ISKANDAR) | Arabic (Oriental) | BnF / Gotha / Leeds | 5,221 |
| RASM | Arabic (Oriental) | British Library *(not redistributed)* | 1,488 |
| [KHATT](https://huggingface.co/datasets/johnlockejrr/KHATT_v1.0_dataset) | Arabic (modern cursive) | KHATT v1.0 | 6,673 |
| [MUHARAF](https://huggingface.co/datasets/aamijar/muharaf-public) | Arabic (modern cursive) | muharaf-public | 24,494 |

Each row carries two source-related columns:

- `dataset` — exact source: `rasam1`, `rasam2`, `tarima`, `baybars`, `iskandar`, `rasm`, `khatt`, `muharaf`
- `transcription_guidelines` — guideline followed during transcription:
  - `rasam` — RASAM-1, RASAM-2, TARIMA, BAYBARS, ISKANDAR
  - `rasm` — RASM
  - `khatt` — KHATT
  - `muharaf` — MUHARAF

Only the `rasam` guidelines have been formally specified and verified. The other datasets are nonetheless highly compatible with them: all sources preserve the text as written in the original document, without normalization, modernization, or editorial correction.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("calfa-ai/AMIDDA")

# Filter by source dataset
baybars_only = ds["train"].filter(lambda x: x["dataset"] == "baybars")

# Filter by transcription guideline
rasam_only   = ds["train"].filter(lambda x: x["transcription_guidelines"] == "rasam")
```

## Results on this dataset

CER (%) per source dataset across the six strategies, one row per model variant, with third-party scores for perspective. "—" means not evaluated (Gemini 3.5 Flash: partial run on three corpora).

| Method | baybars | iskandar | khatt | muharaf | rasam1 | rasam2 | rasm | tarima | Overall |
|:---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| ***(i) VLM API + line-level ICL*** — 280-line subset (35 per corpus), best shot count per model | | | | | | | | | |
| Gemini 3 Flash, 25 shots | 11.2 | 11.8 | 7.5 | 22.5 | 16.5 | 29.0 | 21.1 | 25.6 | 18.3 |
| Gemini 3.5 Flash, 25 shots | 9.6 | 11.0 | 4.8 | — | — | — | — | — | 9.9 |
| Gemma 4 26B IT, 5 shots | 22.9 | 20.7 | 18.8 | 42.5 | 33.1 | 40.8 | 32.2 | 39.3 | 31.6 |
| Mistral Large 2512, 5 shots | 56.3 | 62.9 | 65.0 | 70.3 | 70.4 | 67.8 | 73.7 | 63.3 | 66.7 |
| Mistral Small 2603, 5 shots | 68.7 | 66.5 | 62.6 | 75.3 | 72.3 | 75.2 | 77.6 | 70.3 | 71.1 |
| Qwen 2.5 VL 72B, 5 shots | 22.9 | 28.1 | 21.1 | 49.0 | 43.4 | 43.7 | 42.0 | 53.9 | 38.7 |
| Qwen 3 VL 8B, zero-shot | 53.9 | 47.8 | 104.7 | 89.7 | 96.4 | 108.9 | 57.5 | 82.7 | 83.4 |
| Qwen 3.5 Flash, zero-shot | 32.3 | 26.9 | 41.0 | 55.2 | 55.9 | 56.4 | 40.2 | 55.6 | 46.8 |
| Qwen 3.6 Flash, zero-shot | 31.9 | 38.9 | 36.6 | 55.2 | 60.6 | 60.0 | 41.8 | 59.4 | 49.4 |
| ***(ii) VLM API + page-level context*** — 60-line subset (10 per corpus), best mode per model (KHATT and Muharaf excluded) | | | | | | | | | |
| Gemini 3 Flash, ctx = text | 23.2 | 11.8 | — | — | 26.4 | 32.8 | 10.6 | 26.0 | 21.8 |
| Gemma 4 26B IT, ctx = text | 37.7 | 19.8 | — | — | 45.4 | 45.2 | 21.3 | 46.6 | 36.0 |
| Mistral Large 2512, ctx = `img_text` | 72.8 | 68.4 | — | — | 75.7 | 70.6 | 61.4 | 69.5 | 69.7 |
| Qwen 2.5 VL 72B, ctx = text | 45.8 | 25.1 | — | — | 63.2 | 52.0 | 27.0 | 49.7 | 43.8 |
| Qwen 3.6 Flash, ctx = text | 52.4 | 36.3 | — | — | 67.1 | 57.0 | 63.8 | 76.6 | 58.8 |
| ***(iii) Local zero-shot OCR-VLM*** — full test split | | | | | | | | | |
| GLM-OCR, zero-shot | 199.4 | 189.1 | 247.6 | 295.5 | 383.9 | 329.4 | 290.6 | 359.7 | 286.1 |
| Chandra-OCR-2, zero-shot | 496.4 | 553.8 | 657.9 | 1898.4 | 1536.8 | 1632.2 | 1056.6 | 1877.0 | 1346.2 |
| QARI-OCR v0.3, zero-shot | 140.1 | 113.6 | 50.9 | 105.9 | 114.6 | 100.6 | 71.2 | 85.1 | 104.3 |
| GLM-OCR, LoRA fine-tuned | 120.3 | 137.8 | 60.2 | 107.2 | 175.3 | 178.3 | 230.0 | 159.2 | 125.9 |
| ***(iv) LoRA fine-tuning of Qwen3.5-4B*** — full test split | | | | | | | | | |
| Qwen3.5-4B, r = 64 | 13.4 | 15.9 | 9.8 | 30.1 | 25.0 | 27.6 | 37.4 | 24.2 | 23.1 |
| Qwen3.5-4B, r = 128 | 13.5 | 15.9 | 8.1 | 31.1 | 22.3 | 29.2 | 34.3 | 31.2 | 23.2 |
| ***(v) MoE post-correction*** — 80-line subset, judge output | | | | | | | | | |
| baseline (Qwen3.5-4B FT) | 18.9 | 14.2 | 11.6 | 18.3 | 21.0 | 23.3 | 19.9 | 25.6 | 19.5 |
| Gemini / Gemini / GPT-5.1 | 14.0 | 10.9 | 12.9 | 15.1 | 14.3 | 21.4 | 13.8 | 20.7 | 15.8 |
| Gemini / Qwen3-VL-32B / GPT-5.1 | 15.6 | 10.4 | 11.0 | 16.0 | 17.6 | 21.7 | 21.0 | 20.7 | 17.2 |
| Gemini / Qwen3.5-35B / Mistral-L | 29.7 | 17.2 | 14.3 | 17.7 | 15.5 | 29.8 | 29.0 | 26.6 | 22.3 |
| ***(vi) RNN (CTC), specialised per corpus, already existing*** — per-corpus test splits inherited by AMIDDA | | | | | | | | | |
| CTC, one RNN per corpus (CALFA CRNN model, see RASAM paper) | 7.8 | — | 6.1 | 9.5 | 3.6 | 4.9 | 8.9 | 3.9 | 6.4 |
| ***Third-party baselines*** (reported on their own test splits) | | | | | | | | | |
| HATFormer | — | — | 15.4 | 8.6 | — | — | — | — | — |
| Qalam | — | — | 10.4 † | — | — | — | — | — | — |

Source: AMIDDA -- *Is a Generic Dataset and Foundation VLM for Arabic HTR Worth It? Lessons from AMIDDA* [hal-05693582](https://enc.hal.science/hal-05693582)


## Citation

Presented at HIP 2026 @ ICDAR 2026 (proceedings to come).

Preprint:

```
@unpublished{vidalgorene:hal-05693582,
  TITLE = {{Is a Generic Dataset and Foundation VLM for Arabic HTR Worth It? Lessons from AMIDDA}},
  AUTHOR = {Vidal-Gor{\`e}ne, Chahan and Lucas, No{\"e}mie and Salah, Cl{\'e}ment and Decours-Perez, Ali{\'e}nor},
  URL = {https://enc.hal.science/hal-05693582},
  NOTE = {working paper or preprint},
  YEAR = {2026},
  MONTH = Jul,
  KEYWORDS = {Arabic HTR ; Vision-Language Models ; In-Context Learning ; Data Bootstrapping ; DISTAM},
  PDF = {https://enc.hal.science/hal-05693582v1/file/main.pdf},
  HAL_ID = {hal-05693582},
  HAL_VERSION = {v1},
}
```

## License

Mixed — see each sub-dataset for its own license. Refer to the source repositories.