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Detect and extract tables to markdown and csv
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VikParuchuri/tabled
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Important
Tabled is now deprecated. The functionality here has been migrated tomarker.To extract tables from a PDF with marker, you can runpython convert_single.py FILENAME --converter_cls marker.converters.table.TableConverter
Read more, and see other CLI optionshere.
Tabled is a small library for detecting and extracting tables. It usessurya to find all the tables in a PDF, identifies the rows/columns, and formats cells into markdown, csv, or html.
Characteristic | Population | Change from 2016 to 2060 | ||||||
---|---|---|---|---|---|---|---|---|
2016 | 2020 | 2030 | 2040 | 2050 | 2060 | Number | Percent | |
Total population | 323.1 | 332.6 | 355.1 | 373.5 | 388.9 | 404.5 | 81.4 | 25.2 |
Under 18 years | 73.6 | 74.0 | 75.7 | 77.1 | 78.2 | 80.1 | 6.5 | 8.8 |
18 to 44 years | 116.0 | 119.2 | 125.0 | 126.4 | 129.6 | 132.7 | 16.7 | 14.4 |
45 to 64 years | 84.3 | 83.4 | 81.3 | 89.1 | 95.4 | 97.0 | 12.7 | 15.1 |
65 years and over | 49.2 | 56.1 | 73.1 | 80.8 | 85.7 | 94.7 | 45.4 | 92.3 |
85 years and over | 6.4 | 6.7 | 9.1 | 14.4 | 18.6 | 19.0 | 12.6 | 198.1 |
100 years and over | 0.1 | 0.1 | 0.1 | 0.2 | 0.4 | 0.6 | 0.5 | 618.3 |
Discord is where we discuss future development.
There is a hosted API for tabled availablehere:
- Works with PDF, images, word docs, and powerpoints
- Consistent speed, with no latency spikes
- High reliability and uptime
I want tabled to be as widely accessible as possible, while still funding my development/training costs. Research and personal usage is always okay, but there are some restrictions on commercial usage.
The weights for the models are licensedcc-by-nc-sa-4.0
, but I will waive that for any organization under $5M USD in gross revenue in the most recent 12-month period AND under $5M in lifetime VC/angel funding raised. You also must not be competitive with theDatalab API. If you want to remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the optionshere.
You'll need python 3.10+ and PyTorch. You may need to install the CPU version of torch first if you're not using a Mac or a GPU machine. Seehere for more details.
Install with:
pip install tabled-pdf
Post-install:
- Inspect the settings in
tabled/settings.py
. You can override any settings with environment variables. - Your torch device will be automatically detected, but you can override this. For example,
TORCH_DEVICE=cuda
. - Model weights will automatically download the first time you run tabled.
tabled DATA_PATH
DATA_PATH
can be an image, pdf, or folder of images/pdfs--format
specifies output format for each table (markdown
,html
, orcsv
)--save_json
saves additional row and column information in a json file--save_debug_images
saves images showing the detected rows and columns--skip_detection
means that the images you pass in are all cropped tables and don't need any table detection.--detect_cell_boxes
by default, tabled will attempt to pull cell information out of the pdf. If you instead want cells to be detected by a detection model, specify this (usually you only need this with pdfs that have bad embedded text).--save_images
specifies that images of detected rows/columns and cells should be saved.
After running the script, the output directory will contain folders with the same basenames as the input filenames. Inside those folders will be the markdown files for each table in the source documents. There will also optionally be images of the tables.
There will also be aresults.json
file in the root of the output directory. The file will contain a json dictionary where the keys are the input filenames without extensions. Each value will be a list of dictionaries, one per table in the document. Each table dictionary contains:
cells
- the detected text and bounding boxes for each table cell.bbox
- bbox of the cell within the table bboxtext
- the text of the cellrow_ids
- ids of rows the cell belongs tocol_ids
- ids of columns the cell belongs toorder
- order of this cell within its assigned row/column cell. (sort by row, then column, then order)
rows
- bboxes of the detected rowsbbox
- bbox of the row in (x1, x2, y1, y2) formatrow_id
- unique id of the row
cols
- bboxes of detected columnsbbox
- bbox of the column in (x1, x2, y1, y2) formatcol_id
- unique id of the column
image_bbox
- the bbox for the image in (x1, y1, x2, y2) format. (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner. The table bbox is relative to this.bbox
- the bounding box of the table within the image bbox.pnum
- page number within the documenttnum
- table index on the page
I've included a streamlit app that lets you interactively try tabled on images or PDF files. Run it with:
pip install streamlittabled_gui
fromtabled.extractimportextract_tablesfromtabled.fileinputimportload_pdfs_imagesfromtabled.inference.modelsimportload_detection_models,load_recognition_models,load_layout_modelsdet_models,rec_models,layout_models=load_detection_models(),load_recognition_models(),load_layout_models()images,highres_images,names,text_lines=load_pdfs_images(IN_PATH)page_results=extract_tables(images,highres_images,text_lines,det_models,layout_models,rec_models)
Avg score | Time per table | Total tables |
---|---|---|
0.847 | 0.029 | 688 |
Getting good ground truth data for tables is hard, since you're either constrained to simple layouts that can be heuristically parsed and rendered, or you need to use LLMs, which make mistakes. I chose to use GPT-4 table predictions as a pseudo-ground-truth.
Tabled gets a.847
alignment score when compared to GPT-4, which indicates alignment between the text in table rows/cells. Some of the misalignments are due to GPT-4 mistakes, or small inconsistencies in what GPT-4 considered the borders of the table. In general, extraction quality is quite high.
Running on an A10G with 10GB of VRAM usage and batch size64
, tabled takes.029
seconds per table.
Run the benchmark with:
python benchmarks/benchmark.py out.json
- Thank you toPeter Jansen for the benchmarking dataset, and for discussion about table parsing.
- Huggingface for inference code and model hosting
- PyTorch for training/inference
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Detect and extract tables to markdown and csv