DOM Accessibility Tree Extraction: A Reliable Method for LLMs on Dynamic Web Tables

A method for extracting data from dynamic web tables by using Playwright to access the browser's accessibility tree, which provides structured, semantic data without OCR errors. The approach involves launching a headless browser, waiting for the page to fully load, and using the `inner_text()` method on the table element to capture the data as tab-separated text. The method successfully extracted temperature data from a 472-city weather table in approximately 8 seconds with zero OCR errors, though it requires a real browser runtime and may face limitations with headless automation blocking or canvas-rendered tables.

Status: Current best available technique as of 2026. Treat as standard practice, not a workaround. The Problem Three naive approaches fail on modern sites: - view-source / static fetch — returns server HTML before JavaScript runs. JS-rendered tables show only empty <tbody tags. - Screenshot + OCR — slow, pixel-dependent, brittle, compounds errors on numeric data. - Screenshot + vision model — expensive, context-limited, fails on tables larger than one viewport. Root cause: The web has shifted to client-side rendering. Data lives in JavaScript runtime state, not HTML. The Method Intuition: Programmatic equivalent of: Highlight table → Copy → Paste into Notepad → Import to Excel → Delete irrelevant columns → Sort and count. Steps: - Load page in headless browser Playwright recommended — JavaScript executes, table renders - Interact with any dropdowns or filters, wait for networkidle - Call inner text on the table element - Write extracted text to file audit trail, enables re-parsing - Parse in Python — split on newlines/tabs, cast numerics, filter and count Why It Works The accessibility tree is structured, semantic, not pixel-dependent, already parsed by the browser, and fast. No OCR transcription errors on numbers. Pseudocode python python from playwright.sync api import sync playwright import re with sync playwright as p: browser = p.chromium.launch headless=True page = browser.new page page.goto URL, wait until="networkidle" page.select option "select view-filter", label="All Cities" page.wait for load state "networkidle" table text = page.query selector "table" .inner text browser.close with open "table output.txt", "w" as f: f.write table text lines = table text.strip .split "\n" rows = line.split "\t" for line in lines 1: if line.strip temps = float re.sub r" ^\d.\- ", "", r 2 for r in rows if r 2 .strip print f"Below 32°F: {sum t < 32 for t in temps }" print f"Above 100°F: {sum t 100 for t in temps }" Real Example Source: timeanddate.com, 472-city weather table, “Somewhat Popular” view. • Execution time: ~8 seconds • Cities below 32°F: 47 • Cities above 100°F: 12 • OCR errors: 0 Limitations • Requires real browser runtime Playwright/Puppeteer • Some sites block headless automation • Canvas-rendered tables require page.accessibility.snapshot fallback • Infinite scroll requires simulating scroll events • Always prefer an official API if one exists Full writeup with detailed tips and examples on GitHub: https://github.com/hottbunny/LLM-AI-Perplexity-Skills-and-Updates/blob/hottbunny-tested-works-htmlsearchtablecrawldataretrivalskill/dom extraction method.md