--- title: "AE3-TL" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5e7ef2ee-310c-4e50-b182-2907ccc23049" updated: "2026-11-13" --- # AE3-TL > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds every table on a digital PDF page and rebuilds it cell by cell, with merged cells, headers, captions and footers. For teams pulling tables out of reports, filings and papers. AE3-TL is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by APEXE3HQ ## About ### What it is AE3-TL is a table extraction model from APEXE3HQ with 32M parameters. It looks at a rendered PDF page and the page's own words, then returns each table as a grid of cells. Headers, merged cells, cell text, captions and footers all come back linked to the right table. ### Who it suits Teams that pull tables out of digital PDFs, such as reports, filings, papers and statements, and need the cell text to match the document exactly. The card says it runs on a single GPU or on a CPU through ONNX. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance | Analysts | Pull tables out of filings and statements | Turn a balance sheet page into rows and columns | | Research | Researchers | Extract result tables from papers | Collect the tables from a batch of PDF papers | | Data engineering | Pipeline builders | Run extraction over large document sets | Process a backlog of reports page by page | | Document software | Developers | Add table recognition to a PDF tool | Show each detected table with its caption attached | Skip it if your PDFs are scans without a text layer, because the cell text comes from the PDF's own words rather than from reading the image. ### Where to use it - Python or any program that runs ONNX models - A single GPU, or a CPU for smaller jobs - Windows, macOS or Linux for downloading the files - Document pipelines that already render PDF pages and read their words | Use case | Who | What they get back | | --- | --- | --- | | Table extraction | Analysts | Each table rebuilt cell for cell, with headers and merged cells | | Caption linking | Document teams | Captions and footers tied to the right table on a busy page | | Bulk processing | Engineers | Tables from many pages at the card's reported speed | ### How to set it up You need Python and pip to download, plus a way to run ONNX models. You also need to turn each PDF page into an image and read its words with positions. - Python and pip - An ONNX runtime - A step that renders PDF pages and reads their text layer 1. Install the Hugging Face hub library. 2. Download the repository. 3. Feed it a rendered page and the page's words through your ONNX runtime. ```sh pip install -U huggingface_hub hf download APEXE3HQ/ae3-tl ``` ### How to use it Give the model two inputs per page, the rendered image and the words with their positions. It returns every table on the page. The card points to its code repository for the full run steps. | Tool | What it does | | --- | --- | | Rendered page image | The first input, where the model reads the table structure | | Words with positions | The second input, taken from the PDF's text layer | | Grid output | Each table's cells, headers, text, caption and footer | ### Example You give it a page from an annual report with two tables and their captions. It returns each table as a grid of cells with the merged header cells kept, and links each caption to the table below it. ### For newcomers and experts If you are new, think of it as a tool that sees where a table's lines and cells are and copies the words from the PDF into them. Start with one page that has a single table. For people in the field, the structure comes from the page image while the text comes from the words the PDF already holds, which avoids reading mistakes. On the PubTables-v2 page-level test, 73.7 percent of tables came out as an exact match, against POTATR at 64.5 and Gemini 3.1 Pro at 35.2. Render the PDF page as an image → Read the page's words and positions → Model finds each table's structure → Cells, captions and footers come back ### Good to know Tables reproduced exactly, percent of 9,047 test tables | Label | Value | | --- | --- | | AE3-TL | 73.7% | | POTATR | 64.5% | | Gemini 3.1 Pro | 35.2% | - License: Apache 2.0, including commercial use as the card states - Cost to run: none named, though the card's own figure is about $30 per million pages on a rented A10 GPU - Data it reaches: it downloads files from Hugging Face, then reads the page images and words you give it - Not for: scanned PDFs with no text layer - Speed reached about 80,808 pages per hour when measured on a single A10 GPU ## README ````markdown # AE3-TL Finds every table on a digital PDF page and rebuilds it cell by cell, with merged cells, headers, captions and footers. For teams pulling tables out of reports, filings and papers. Made by APEXE3HQ on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AE3-TL is a table extraction model from APEXE3HQ with 32M parameters. It looks at a rendered PDF page and the page's own words, then returns each table as a grid of cells. Headers, merged cells, cell text, captions and footers all come back linked to the right table. Who it suits: Teams that pull tables out of digital PDFs, such as reports, filings, papers and statements, and need the cell text to match the document exactly. The card says it runs on a single GPU or on a CPU through ONNX. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Finance | Analysts | Pull tables out of filings and statements | Turn a balance sheet page into rows and columns Research | Researchers | Extract result tables from papers | Collect the tables from a batch of PDF papers Data engineering | Pipeline builders | Run extraction over large document sets | Process a backlog of reports page by page Document software | Developers | Add table recognition to a PDF tool | Show each detected table with its caption attached ``` Skip it if your PDFs are scans without a text layer, because the cell text comes from the PDF's own words rather than from reading the image. Where to use it: - Python or any program that runs ONNX models - A single GPU, or a CPU for smaller jobs - Windows, macOS or Linux for downloading the files - Document pipelines that already render PDF pages and read their words ```table Use case | Who | What they get back Table extraction | Analysts | Each table rebuilt cell for cell, with headers and merged cells Caption linking | Document teams | Captions and footers tied to the right table on a busy page Bulk processing | Engineers | Tables from many pages at the card's reported speed ``` How to set it up: You need Python and pip to download, plus a way to run ONNX models. You also need to turn each PDF page into an image and read its words with positions. - Python and pip - An ONNX runtime - A step that renders PDF pages and reads their text layer 1. Install the Hugging Face hub library. 2. Download the repository. 3. Feed it a rendered page and the page's words through your ONNX runtime. ```sh pip install -U huggingface_hub hf download APEXE3HQ/ae3-tl ``` How to use it: Give the model two inputs per page, the rendered image and the words with their positions. It returns every table on the page. The card points to its code repository for the full run steps. ```table Tool | What it does Rendered page image | The first input, where the model reads the table structure Words with positions | The second input, taken from the PDF's text layer Grid output | Each table's cells, headers, text, caption and footer ``` Example: You give it a page from an annual report with two tables and their captions. It returns each table as a grid of cells with the merged header cells kept, and links each caption to the table below it. For newcomers and experts: If you are new, think of it as a tool that sees where a table's lines and cells are and copies the words from the PDF into them. Start with one page that has a single table. For people in the field, the structure comes from the page image while the text comes from the words the PDF already holds, which avoids reading mistakes. On the PubTables-v2 page-level test, 73.7 percent of tables came out as an exact match, against POTATR at 64.5 and Gemini 3.1 Pro at 35.2. ```flow Render the PDF page as an image Read the page's words and positions Model finds each table's structure Cells, captions and footers come back ``` Good to know: ```chart Title: Tables reproduced exactly, percent of 9,047 test tables AE3-TL: 73.7% POTATR: 64.5% Gemini 3.1 Pro: 35.2% ``` - License: Apache 2.0, including commercial use as the card states - Cost to run: none named, though the card's own figure is about $30 per million pages on a rented A10 GPU - Data it reaches: it downloads files from Hugging Face, then reads the page images and words you give it - Not for: scanned PDFs with no text layer - Speed reached about 80,808 pages per hour when measured on a single A10 GPU ## On Hugging Face, 13 November 2026 - Task: object detection - Library: onnx - Downloads in the last 30 days: 16 - Likes: 6 - License: apache-2.0 - Languages: en - Tags: onnx, coreml, table-extraction, table-structure-recognition, document-ai, d-fine, object-detection ## Links - Model: https://huggingface.co/APEXE3HQ/ae3-tl - Files: https://huggingface.co/APEXE3HQ/ae3-tl/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download APEXE3HQ/ae3-tl ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [APEXE3HQ/ae3-tl on Hugging Face](https://huggingface.co/APEXE3HQ/ae3-tl) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AE3-TL (this page) | 16 | 6 | — | apache-2.0 | 2026-11-04 | | [Dolphin](https://mindket.com/p/10a017cd-8097-462e-97a0-d94807f69274) | 469 | 521 | 398M | mit | 2025-07-16 | | [Rukopys-OCR-4B](https://mindket.com/p/4e99b935-7cb7-43c0-aadb-c7fea869fbe2) | 86 | 7 | 4.5B | apache-2.0 | 2026-07-30 | | [Orukeet](https://mindket.com/p/01ec445a-061b-4b1f-a0e9-91cef756f183) | 35,262 | 110 | 627M | cc-by-sa-4.0 | 2026-11-11 | | [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 | | [Applio by IAHispano](https://mindket.com/p/3e2deb09-3811-4536-a045-d10b219cf7c7) | 334 | 229 | — | mit | 2026-10-26 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-04 - First published: 2026-10-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open APEXE3HQ/ae3-tl on Hugging Face](https://huggingface.co/APEXE3HQ/ae3-tl) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5e7ef2ee-310c-4e50-b182-2907ccc23049) - [Its page on Mindket](https://mindket.com/p/5e7ef2ee-310c-4e50-b182-2907ccc23049)