---
language:
  - de
  - en
  - es
  - fr
  - ru
task_categories:
  - text-retrieval
tags:
  - ocr
  - information-retrieval
  - noisy-text
  - miracl
  - benchmark
  - ocr-simulation
size_categories:
  - 10K<n<100K
---

# OCR-MIRACL

An OCR-degraded version of the [MIRACL](https://project-miracl.github.io/) multilingual retrieval benchmark (`miracl/miracl`), designed to evaluate embedding models on **noisy, OCR-like text**.

## Dataset Description

A 2,000-document subsample per language was drawn from `miracl/miracl` (dev split).
Each passage and query was rendered as a PDF at a specific DPI / font-size setting and
re-extracted via OCR using the
[ocr-robust-multilingual-embeddings](https://github.com/impresso/ocr-robust-multilingual-embeddings/tree/main/ocr_simulator/ocr_simulator)
OCR simulator to introduce realistic character-level noise.

Both the **original clean text** and the **OCR-noised text** are provided,
together with the original MIRACL relevance judgments (qrels).

### Configurations

| Config | DPI | Font Size | Description |
|---|---|---|---|
| `{lang}_dpi120_font10` | 120 | 10 pt | Low quality — high noise |
| `{lang}_dpi130_font10` | 130 | 10 pt | Medium quality |
| `{lang}_dpi300_font12` | 300 | 12 pt | High quality — low noise |

Languages: `de`, `en`, `es`, `fr`, `ru`

### Configs per Language/DPI

Each (language, DPI) combination provides three configs:

| Config | Columns | Description |
|---|---|---|
| `{lang}_{dpi}_corpus` | `_id`, `clean_text`, `ocr_text` | Document passages |
| `{lang}_{dpi}_queries` | `_id`, `clean_text`, `ocr_text` | Search queries |
| `{lang}_{dpi}_qrels` | `query_id`, `corpus_id`, `score` | Relevance judgments |

All configs use a single split: `test`.

### Sample Sizes

| Language | Corpus | Queries |
|---|---|---|
| de | 2,000 | 305 |
| en | 2,296 | 799 |
| es | 2,976 | 648 |
| fr | 2,000 | 343 |
| ru | 3,441 | 1,252 |

## Usage

```python
from datasets import load_dataset

# Load English at DPI 120
corpus  = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_corpus",  split="test")
queries = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_queries", split="test")
qrels   = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_qrels",   split="test")

# Access clean and OCR-noised text
print(corpus[0]["clean_text"])
print(corpus[0]["ocr_text"])
```

## Evaluation

### Quick Start (Python)

```python
import numpy as np
from datasets import load_dataset
from sentence_transformers import SentenceTransformer

# 1. Load data
corpus  = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_corpus",  split="test")
queries = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_queries", split="test")
qrels   = load_dataset("YOUR_ORG/ocr-miracl", data_dir="data/en_dpi120_font10_qrels",   split="test")

# 2. Encode with any SentenceTransformer model
model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
corpus_emb  = model.encode(corpus["ocr_text"],  normalize_embeddings=True, show_progress_bar=True)
query_emb   = model.encode(queries["ocr_text"], normalize_embeddings=True, show_progress_bar=True)

# 3. Retrieve — cosine similarity (embeddings are L2-normalised)
similarities = np.dot(query_emb, corpus_emb.T)

# 4. Compute NDCG@10 for each query
qrels_dict = {}
for row in qrels:
    qrels_dict.setdefault(row["query_id"], {})[row["corpus_id"]] = row["score"]

corpus_ids = corpus["_id"]
for i, qid in enumerate(queries["_id"]):
    if qid not in qrels_dict:
        continue
    top_idx = np.argsort(similarities[i])[::-1][:10]
    hits = [qrels_dict[qid].get(corpus_ids[j], 0) for j in top_idx]
    dcg  = sum(r / np.log2(k + 2) for k, r in enumerate(hits))
    ideal = sorted(hits, reverse=True)
    idcg = sum(r / np.log2(k + 2) for k, r in enumerate(ideal))
    print(f"Query {qid}: NDCG@10 = {dcg / idcg if idcg else 0:.4f}")
```

### Full Evaluation Script

The included `evaluation_IR.py` evaluates across all languages, DPI settings, and modes in one run:

```bash
pip install datasets sentence-transformers torch numpy pandas

python evaluation_IR.py \
    --model Alibaba-NLP/gte-multilingual-base \
    --dataset YOUR_ORG/ocr-miracl \
    --dpi dpi120_font10 dpi130_font10 dpi300_font12 \
    --langs de en es fr ru \
    --mode clean ocr \
    --batch_size 64
```

Results are saved to `./ir_results/results_latest.csv`.

### Evaluation Modes

| Mode | Corpus | Queries | Purpose |
|---|---|---|---|
| `clean` | clean_text | clean_text | Upper bound (no OCR noise) |
| `ocr` | ocr_text | ocr_text | Realistic full-OCR scenario |
| `clean2ocr` | clean_text | ocr_text | Noisy user query against clean index |
| `ocr2clean` | ocr_text | clean_text | Clean query against noisy OCR index |

### Metrics

- **NDCG@10** — Normalized Discounted Cumulative Gain
- **MRR@10** — Mean Reciprocal Rank
- **Recall@100**

## CER Summary (Character Error Rate)

Mean character-level error rates between clean and OCR-noised text, measured per language and DPI setting.

### dpi120_font10 (Low quality)

| Language | Corpus CER | Query CER | Corpus Docs | Queries |
|---|---|---|---|---|
| de | 12.8% | 4.7% | 2,000 | 305 |
| en | 9.4% | 5.8% | 2,296 | 799 |
| es | 8.8% | 4.4% | 2,976 | 648 |
| fr | 9.8% | 7.1% | 2,000 | 343 |
| ru | 10.1% | 7.0% | 3,441 | 1,252 |

### dpi130_font10 (Medium quality)

| Language | Corpus CER | Query CER | Corpus Docs | Queries |
|---|---|---|---|---|
| de | 7.3% | 4.0% | 2,000 | 305 |
| en | 5.2% | 2.9% | 2,296 | 799 |
| es | 4.7% | 2.9% | 2,976 | 648 |
| fr | 5.7% | 5.2% | 2,000 | 343 |
| ru | 6.4% | 5.7% | 3,441 | 1,252 |

### dpi300_font12 (High quality)

| Language | Corpus CER | Query CER | Corpus Docs | Queries |
|---|---|---|---|---|
| de | 1.8% | 1.2% | 2,000 | 305 |
| en | 1.5% | 0.9% | 2,296 | 799 |
| es | 1.4% | 0.7% | 2,976 | 648 |
| fr | 1.8% | 1.1% | 2,000 | 343 |
| ru | 2.9% | 3.0% | 3,441 | 1,252 |


## OCR Noise Generation

Noise was generated with the [OCR Simulator](https://github.com/impresso/ocr-robust-multilingual-embeddings/tree/main/ocr_simulator/ocr_simulator)
using `generate_ocr_miracl.py`:

1. Texts are split into sentences.
2. Each sentence is rendered as a PDF image (Pillow) at the given DPI / font size.
3. The image is OCR-ed via Tesseract.
4. Sentences are rejoined into documents.

## Related

- **[OCR-MLDR](https://huggingface.co/datasets/YOUR_ORG/ocr-mldr)** — Same OCR noise pipeline applied to `mteb/MultiLongDocRetrieval`.

## Citation

If you use this dataset, please cite the OCR noise generation method and the original MIRACL benchmark:

```bibtex
@inproceedings{michail-etal-2025-cheap,
    title     = "Cheap Character Noise for {OCR}-Robust Multilingual Embeddings",
    author    = "Michail, Andrianos  and
                 Opitz, Juri  and
                 Wang, Yining  and
                 Meister, Robin  and
                 Sennrich, Rico  and
                 Clematide, Simon",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month     = jul,
    year      = "2025",
    address   = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url       = "https://aclanthology.org/2025.findings-acl.609/",
    pages     = "11705--11716",
    ISBN      = "979-8-89176-256-5"
}

@article{zhang2022miracl,
  title={Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages},
  author={Zhang, Xinyu and Thakur, Nandan and Ogundepo, Odunayo and Kamalloo, Ehsan and Alfonso-Hermelo, David and Li, Xiaoguang and Liu, Qun and Rezagholizadeh, Mehdi and Lin, Jimmy},
  journal={arXiv preprint arXiv:2210.09984},
  year={2022}
}
```