OCRFlux is a multimodal large language model based toolkit for converting PDFs and images into clean, readable, plain Markdown text. It aims to push the current state-of-the-art to a significantly higher level.
Try the online demo: OCRFlux Demo
Functions: Whole file parsing
-
On each page
- Convert into text with a natural reading order, even in the presence of multi-column layouts, figures, and insets
- Support for complicated tables and equations
- Automatically removes headers and footers
-
Cross-page table/paragraph merging
- Cross-page table merging
- Cross-page paragraph merging
Key features:
-
Superior parsing quality on each page
It respectively achieves 0.095 higher (from 0.872 to 0.967), 0.109 higher (from 0.858 to 0.967) and 0.187 higher (from 0.780 to 0.967) Edit Distance Similarity (EDS) on our released benchmark OCRFlux-bench-single than the baseline model olmOCR-7B-0225-preview, Nanonets-OCR-s and MonkeyOCR.
-
Native support for cross-page table/paragraph merging (to our best this is the first to support this feature in all the open sourced project).
-
Based on a 3B parameter VLM, so it can run even on GTX 3090 GPU.
Release:
- OCRFlux-3B - 3B parameter VLM
- Benchmark for evaluation
- Jun 17, 2025 - v0.1.0 - Initial public launch and demo.
We ship two comprehensive benchmarks to help measure the performance of our OCR system in single-page parsing:
-
OCRFlux-bench-single: Containing 2000 pdf pages (1000 English pages and 1000 Chinese pages) and their ground-truth Markdowns (manually labeled with multi-round check).
-
OCRFlux-pubtabnet-single: Derived from the public PubTabNet benchmark with some format transformation. It contains 9064 HTML table samples, which are split into simple tables and complex tables according to whether they have rowspan and colspan cells.
We emphasize that the released benchmarks are NOT included in our training and evaluation data. The following is the main result:
-
In OCRFlux-bench-single, we calculated the Edit Distance Similarity (EDS) between the generated Markdowns and the ground-truth Markdowns as the metric.
Language Model Avg EDS ↑ English olmOCR-7B-0225-preview 0.885 Nanonets-OCR-s 0.870 MonkeyOCR 0.828 OCRFlux-3B 0.971 Chinese olmOCR-7B-0225-preview 0.859 Nanonets-OCR-s 0.846 MonkeyOCR 0.731 OCRFlux-3B 0.962 Total olmOCR-7B-0225-preview 0.872 Nanonets-OCR-s 0.858 MonkeyOCR 0.780 OCRFlux-3B 0.967 -
In OCRFlux-pubtabnet-single, we calculated the Tree Edit Distance-based Similarity (TEDS) between the generated HTML tables and the ground-truth HTML tables as the metric.
Type Model Avg TEDS ↑ Simple olmOCR-7B-0225-preview 0.810 Nanonets-OCR-s 0.882 MonkeyOCR 0.880 OCRFlux-3B 0.912 Complex olmOCR-7B-0225-preview 0.676 Nanonets-OCR-s 0.772 MonkeyOCR 0.826 OCRFlux-3B 0.807 Total olmOCR-7B-0225-preview 0.744 Nanonets-OCR-s 0.828 MonkeyOCR 0.853 OCRFlux-3B 0.861
We also conduct some case studies to show the superiority of our model in the blog article.
PDF documents are typically paginated, which often results in tables or paragraphs being split across consecutive pages. Accurately detecting and merging such cross-page structures is crucial to avoid generating incomplete or fragmented content.
The detection task can be formulated as follows: given the Markdowns of two consecutive pages—each structured as a list of Markdown elements (e.g., paragraphs and tables)—the goal is to identify the indexes of elements that should be merged across the pages.
Then for the merging task, if the elements to be merged are paragraphs, we can just concate them. However, for two table fragments, their merging is much more challenging. For example, the table spanning multiple pages will repeat the header of the first page on the second page. Another difficult scenario is that the table cell contains long content that spans multiple lines within the cell, with the first few lines appearing on the previous page and the remaining lines continuing on the next page. We also observe some cases where tables with a large number of columns are split vertically and placed on two consecutive pages. More examples of cross-page tables can be found in our blog article. To address these issues, we develop the LLM model for cross-page table merging. Specifically, this model takes two split table fragments as input and generates a complete, well-structured table as output.
We ship two comprehensive benchmarks to help measure the performance of our OCR system in cross-page table/paragraph detection and merging tasks respectively:
-
OCRFlux-bench-cross: Containing 1000 samples (500 English samples and 500 Chinese samples), each sample contains the Markdown element lists of two consecutive pages, along with the indexes of elements that need to be merged (manually labeled through multiple rounds of review). If no tables or paragraphs require merging, the indexes in the annotation data are left empty.
-
OCRFlux-pubtabnet-cross: Containing 9064 pairs of split table fragments, along with their corresponding ground-truth merged versions.
The released benchmarks are NOT included in our training and evaluation data neither. The following is the main result:
-
In OCRFlux-bench-cross, we caculated the Accuracy, Precision, Recall and F1 score as the metric. Notice that the detection results are right only when it accurately judges whether there are elements that need to be merged across the two pages and output the right indexes of them.
Language Precision ↑ Recall ↑ F1 ↑ Accuracy ↑ English 0.992 0.964 0.978 0.978 Chinese 1.000 0.988 0.994 0.994 Total 0.996 0.976 0.986 0.986 -
In OCRFlux-pubtabnet-cross, we calculate the Tree Edit Distance-based Similarity (TEDS) between the generated merged table and the ground-truth merged table as the metric.
Table type Avg TEDS ↑ Simple 0.965 Complex 0.935 Total 0.950
Requirements:
- Recent NVIDIA GPU (tested on RTX 3090, 4090, L40S, A100, H100) with at least 12 GB of GPU RAM
- 20GB of free disk space
You will need to install poppler-utils and additional fonts for rendering PDF images.
Install dependencies (Ubuntu/Debian)
sudo apt-get update
sudo apt-get install poppler-utils poppler-data ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetoolsSet up a conda environment and install OCRFlux. The requirements for running OCRFlux are difficult to install in an existing python environment, so please do make a clean python environment to install into.
conda create -n ocrflux python=3.11
conda activate ocrflux
git clone https://github.com/chatdoc-com/OCRFlux.git
cd ocrflux
pip install -e . --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer/For quick testing, try the web demo. To run locally, a GPU is required, as inference is powered by vllm under the hood.
-
For a pdf document:
python -m ocrflux.pipeline ./localworkspace --data test.pdf --model /model_dir/OCRFlux-3B
-
For an image:
python -m ocrflux.pipeline ./localworkspace --data test_page.png --model /model_dir/OCRFlux-3B
-
For a directory of pdf or images:
python -m ocrflux.pipeline ./localworkspace --data test_pdf_dir/* --model /model_dir/OCRFlux-3B
You can set --skip_cross_page_merge to skip the cross-page merging in the parsing process to accelerate, it would simply concatenate the parsing results of each page to generate final Markdown of the document.
Results will be stored as JSONL files in the ./localworkspace/results directory.
Each line in JSONL files is a json object with the following fields:
{
"orig_path": str, # the path to the raw pdf or image file
"num_pages": int, # the number of pages in the pdf file
"document_text": str, # the Markdown text of the converted pdf or image file
"page_texts": dict, # the Markdown texts of each page in the pdf file, the key is the page index and the value is the Markdown text of the page
"fallback_pages": [int], # the page indexes that are not converted successfully
}
You can use the inference API to directly call OCRFlux in your codes without using an online vllm server like following:
from vllm import LLM
from ocrflux.inference import parse
file_path = 'test.pdf'
# file_path = 'test.png'
llm = LLM(model="model_dir/OCRFlux-3B",gpu_memory_utilization=0.8,max_model_len=8192)
result = parse(llm,file_path)
if result != None:
document_markdown = result['document_text']
print(document_markdown)
with open('test.md','w') as f:
f.write(document_markdown)
else:
print("Parse failed.")
If parsing is failed or there are fallback pages in the result, you can try to set the argument max_page_retries for the parse function with a positive integer to get a better result. But it may cause longer inference time.
Requirements:
- Docker with GPU support (NVIDIA Toolkit)
- Pre-downloaded model: OCRFlux-3B
To use OCRFlux in a docker container, you can use the following example command:
docker run -it --gpus all \
-v /path/to/localworkspace:/localworkspace \
-v /path/to/test_pdf_dir:/test_pdf_dir/ \
-v /path/to/OCRFlux-3B:/OCRFlux-3B \
chatdoc/ocrflux:latest /localworkspace --data /test_pdf_dir/* --model /OCRFlux-3B/Generate the final Markdown files by running the following command. Generated Markdown files will be in ./localworkspace/markdowns/DOCUMENT_NAME directory.
python -m ocrflux.jsonl_to_markdown ./localworkspacepython -m ocrflux.pipeline --help
usage: pipeline.py [-h] [--task {pdf2markdown,merge_pages,merge_tables}] [--data [DATA ...]] [--pages_per_group PAGES_PER_GROUP] [--max_page_retries MAX_PAGE_RETRIES]
[--max_page_error_rate MAX_PAGE_ERROR_RATE] [--workers WORKERS] [--model MODEL] [--model_max_context MODEL_MAX_CONTEXT] [--model_chat_template MODEL_CHAT_TEMPLATE]
[--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM] [--skip_cross_page_merge] [--port PORT]
workspace
Manager for running millions of PDFs through a batch inference pipeline
positional arguments:
workspace The filesystem path where work will be stored, can be a local folder
options:
-h, --help show this help message and exit
--data [DATA ...] List of paths to files to process
--pages_per_group PAGES_PER_GROUP
Aiming for this many pdf pages per work item group
--max_page_retries MAX_PAGE_RETRIES
Max number of times we will retry rendering a page
--max_page_error_rate MAX_PAGE_ERROR_RATE
Rate of allowable failed pages in a document, 1/250 by default
--workers WORKERS Number of workers to run at a time
--model MODEL The path to the model
--model_max_context MODEL_MAX_CONTEXT
Maximum context length that the model was fine tuned under
--model_chat_template MODEL_CHAT_TEMPLATE
Chat template to pass to vllm server
--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM
Dimension on longest side to use for rendering the pdf pages
--skip_cross_page_merge
Whether to skip cross-page merging
--port PORT Port to use for the VLLM serverThere are some nice reusable pieces of the code that may be useful for your own projects:
- Processing millions of PDFs through our released model using VLLM - pipeline.py
- Generating final Markdowns from jsonl files - jsonl_to_markdown.py
- Evaluating the model on the single-page parsing task - eval_page_to_markdown.py
- Evaluating the model on the table parising task - eval_table_to_html.py
- Evaluating the model on the paragraphs/tables merging detection task - eval_element_merge_detect.py
- Evaluating the model on the table merging task - eval_html_table_merge.py
OCRFlux is developed and maintained by the ChatDOC team, backed by ChatDOC.
OCRFlux is licensed under Apache 2.0. A full copy of the license can be found on GitHub.