Form 13F OCR

[ Form 13F OCR ]

Automate Form 13F OCR to Extract Filings Data Fast

Use LlamaParse to turn 13F PDFs into clean, validated JSON with layout-aware accuracy.

Parse SEC Form 13F Filings into Structured Data

LlamaParse turns messy SEC 13F PDFs into clean, structured JSON or tables you can trust for downstream analysis and monitoring. It reads layout and holdings tables with validation loops and confidence metadata, so you spend less time fixing extracts.

Best-in-Class Accuracy

Form 13F OCR for Institutional Holdings Analysis

Hedge Funds & Asset Management

Turn scanned and inconsistent Form 13F filings into clean, structured JSON and Markdown so analysts can compare managers, issuers, and position changes without spreadsheet cleanup. LlamaParse preserves table structure and CUSIP/ticker context across multi-column layouts, enabling faster signal generation and more reliable backtests.

Financial Compliance & Regulatory Intelligence

Automatically extract holdings, filing dates, and amendment details from Form 13F PDFs with page-level citations and confidence scores to support audit-ready reviews. LlamaParse reduces exception handling by using validation loops to catch misread tables and formatting drift across filers, improving straight-through processing for monitoring workflows.

Market Data & Fintech Platforms

Ingest high-volume 13F documents at scale and standardize them into an API-friendly schema for downstream products like ownership dashboards, alerting, and entity resolution. LlamaParse’s tier-based processing routes simple pages cheaply and reserves heavier parsing for complex tables, keeping unit economics predictable as customer volume grows.

Startups

Ship an MVP that converts messy Form 13F PDFs into a normalized dataset without building brittle parsing code or manual QA pipelines. With natural-language extraction instructions and JSON mode, small teams can iterate on fields (CUSIP, shares, discretion, voting authority) in hours and plug results directly into a database or app.

The Solution

Form 13F OCR Features Built for Accurate, Audit-Ready Extraction

01

Layout-Aware Table Parsing

LlamaParse preserves reading order and structure across multi-column SEC filings and dense tables, so Form 13F data doesn’t get scrambled during extraction. That means you can reliably capture issuer names, CUSIPs, share amounts, and value columns without brittle post-processing.

02

Structured JSON Output Mode

LlamaParse can return Form 13F content as clean, machine-ready JSON instead of loose text, making it straightforward to map holdings into your database schema. This reduces manual cleanup and speeds up downstream reconciliation, analytics, and audit workflows.

03

Citations and Confidence Metadata

Every extracted element can include traceable metadata like page references and spatial coordinates, so you can verify exactly where a holding was read from in the filing. This is critical for compliance-grade 13F pipelines where you need quick spot checks and human-in-the-loop review on low-confidence rows.

04

Auto Correction Validation Loops

LlamaParse uses built-in validation and self-correction loops to catch common extraction errors like misread tickers, broken row alignment, or swapped numeric columns. You get higher straight-through processing on messy scans and fewer exceptions when processing 13F batches at scale.

Technical OCR documentation

Agentic OCR, documented for builders.

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Eliminate Human Error

Our AI catches the typos that tired eyes miss.

Format Flexibility

Export to Excel, JSON, XML, or directly via API.

Enterprise-Grade Security

SOC2 Type II compliant with end-to-end encryption.

No-Code Templates

Train the tool on your specific forms in minutes, not days.

Lightning Speed

Average processing time of <3 seconds per page.

LlamaParse’s support of a wide variety of filetypes and its accuracy of parsing made it the best tool we tested in our evaluations. The LlamaIndex team was very responsive and we were off to the races within a day.

Turn data chaos into data clarity.

Parse your documents free. 10,000 credits to start.

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Common FAQs

How Does it Work?

01

Will the OCR keep Form 13F tables in the right order across multi-column layouts?

Yes—layout-aware parsing preserves reading order and table structure even in dense, multi-column SEC filings. That means issuer names, CUSIPs, share amounts, and value columns stay aligned so your holdings don’t get scrambled.

02

Can I get Form 13F holdings as structured JSON instead of messy text?

You can output clean, machine-ready JSON that maps directly into your database schema. This cuts down manual cleanup and accelerates reconciliation, analytics, and downstream QA.

03

How do I verify where each extracted holding came from in the filing?

Each extracted field can include citations and confidence metadata such as page references and spatial coordinates. This makes spot checks fast and supports compliance workflows when you need to trace values back to the source.

04

What happens when the filing is a low-quality scan or the table rows are misaligned?

Auto-correction validation loops help catch common OCR issues like broken row alignment and swapped numeric columns. You get higher straight-through processing and fewer exceptions when processing large 13F batches.

05

How does it handle common 13F edge cases like variants in issuer names or inconsistent formatting?

The parser focuses on preserving structure first, so formatting differences are less likely to break extraction. Combined with validation and confidence signals, it’s easier to standardize holdings and flag only the rows that truly need review.

06

Can I confidently use this in a compliance-grade 13F pipeline with human-in-the-loop review?

Yes—confidence scores and citations make it simple to route low-confidence rows to reviewers while auto-approving clean extractions. This gives you audit-friendly traceability without slowing down your day-to-day processing.