| ---
|
| 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}
|
| }
|
| ```
|
| |
