OCR Benchmark for Document Image Analysis

OCRBench is a multimodal dataset containing images and text, accepted for publication in Science China Information Sciences. It was created by author echo840 and last updated in December 2024. Specific details on row count, column structure, and file formats are not provided in the input.

Use Cases

• Train multimodal models on image and text data for optical character recognition tasks.
• Benchmark OCR system performance using the dataset's image and text modalities.
• Analyze text extraction accuracy from document images.

Strengths

• Dataset is associated with a peer-reviewed publication in Science China Information Sciences.
• Includes multiple data modalities: image and text.
• Last updated in December 2024, indicating recent maintenance.

Limitations

• Unknown sample size and data volume, making suitability for large-scale training unclear.
• Lack of column descriptions or sample data prevents assessment of feature granularity and structure.
• Geographic and temporal coverage of the source documents are unspecified.

Provenance

Source

huggingface, author echo840

Freshness

Last updated on 2024-12-18.

Geography

Region tag indicates 'us'.