TheFinAI/MultiFinBen-EnglishOCR

---

Dataset Card for EnglishOCR Dataset

Dataset Summary

The EnglishOCR dataset contains images derived from regulatory documents from SEC EDGAR company filings. This dataset is used for benchmarkingg and evaluating Large Language Models ability on converting unstructured dcuments, such as pdfs and images, into machine readable format, particularly in finance domain, where the conversion task is more complex and valuable.

Supported Tasks

• Task: Information Extraction
• Evaluation Metrics: ROUGE-1

Languages

• English

Dataset Structure

Data Instances

Each instance in the English OCR dataset comprises 2 fields:

• image : image of company filings in 2025 (10-K,10-Q), each image represent one page in pdf
• text: ground truth of text

Data Fields

• image : string - Base64-encoded png
• text: extracted text from pdf files

Dataset Creation

Curation Rationale

The EnglishOCR dataset was curated to support research and development on information extraction techniques and layout retain ability for unstructured documents in financial domain in English. By providing real-world company filings in unstructured format with ground truth, the dataset seeks to address challenges in extracting informat as well as layouts of complex documents, and convert into machine-readable format.

Source Data

Initial Data Collection and Normalization

• The source data are company filings from SEC EDGAR system.
• Subset of company filings in 2025 are downloaded in html format, and converted into correspondig pdf versions.
• The pdf files are splited and converted into images, and text of the images are extracted.
• The extracted text are used for matching html chunk and correct image.

Who are the Source Language Producers?

• The source data are regulatory documents from SEC EDGAR system: https://www.sec.gov/search-filings

Annotations

Annotation Process

• The dataset was prepared by collecting, matching, spliting, and converting regulatory documents in English
• No further manual annotations were applied beyond the original curation process.

Who are the Annotators?

• The dataset stems from publicly available company filings.
• No external annotation team was involved beyond this.

Personal and Sensitive Information

• The EnglishOCR dataset does not contain any personally identifiable information (PII) and is strictly focused on Greek text data for summarization purposes.

Considerations for Using the Data

Social Impact of Dataset

This dataset enables AI models to extract structured information from scanned financial documents in multiple languages, promoting transparency and accessibility. By aligning page-level PDF images with accurate ground truth text, it supports the development of fairer, more inclusive models that work across diverse formats and languages.

Discussion of Biases

• The source data is limited to company filings, it may underrepresent other financial document types such as tax records, bank statements, or private company reports, potentially limiting model generalizability.

Other Known Limitations

• The matching process is based on similarity scores between OCR extracted text from image and html chunk, which may cause inaccuracies, or lost of information in matching process, potentially affecting training quality and evaluation reliability.
• While the dataset covers company filings, it may lack sufficient variety in layout styles (e.g., handwritten notes, non-standard financial forms, embedded charts), which could limit a model’s ability to generalize to less structured or unconventional financial documents.

Additional Information

Dataset Curators

• Yueru He

Licensing Information

• License: CC BY 4.0

Citation Information

If you use this dataset in your research, please consider citing the following paper: