OmniDocBench

English | 简体中文 OmniDocBench is an evaluation dataset for diverse document parsing in real-world scenarios, with the following characteristics:

Diverse Document Types: The evaluation set contains 1355 PDF pages, covering 9 document types, 4 layout types and 3 language types. It has broad coverage including academic papers, financial reports, newspapers, textbooks, handwritten notes, etc. Rich Annotations: Contains location information for 15 block-level (text paragraphs… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/OmniDocBench.

Forked from opendatalab/OmniDocBench.

  Sampler

We have added a simple Python tool for filtering and performing stratified sampling on OmniDocBench data.

  Features

Filter JSON entries based on custom criteria Perform stratified sampling based on multiple categories Handle nested JSON fields

  Installation





  Local Development Install (Recommended)

git clone https://huggingface.co/Quivr/OmniDocBench.git cd OmniDocBench pip install -r requirements.txt #… See the full description on the dataset page: https://huggingface.co/datasets/Quivr/OmniDocBench.

ZH_EN_RecBench

zh_en_rec_bench is a benchmark dataset designed to evaluate the robustness and generalization capabilities of text recognition models across multiple scenarios and both Chinese and English scripts. It is constructed by sampling and manually correcting subsets of data from OmniDocBench and TC-STR, with erroneous ground truth labels revised to ensure high-quality evaluation.

  Dataset Overview

This benchmark includes four distinct text recognition scenarios:… See the full description on the dataset page: https://huggingface.co/datasets/puhuilab/zh_en_rec_bench.