Extract structured data from insurance forms, tax forms, intake forms, and government filings into organized Excel columns — no templates required
Organizations across every industry still rely on forms as their primary data collection method. Insurance companies process thousands of claim forms per week. Healthcare facilities collect patient intake forms in waiting rooms. Government agencies receive regulatory filings, license applications, and tax documents by mail and in person. The data on these forms needs to end up in a spreadsheet or database for processing, analysis, and record-keeping — and the gap between a filled-out form and a structured Excel file is where most organizations lose hours of staff time to manual data entry.
The core challenge is that forms are designed for humans to fill out, not for machines to read. A single form can contain printed labels, handwritten text, checkboxes, radio buttons, date fields, signature blocks, and free-text comment areas. The layout varies between organizations, departments, and even form versions within the same company. A generic PDF-to-Excel tool that relies on text extraction or table detection misses most of this structure entirely, because form fields are positioned spatially rather than arranged in neat rows and columns.
Lido uses layout-agnostic AI to read forms the way a human data entry clerk would — identifying field labels, reading the corresponding entries (whether typed, handwritten, or checked), and mapping each value to a structured Excel column. It works across form types and organizations without requiring templates or per-form configuration. Start with 50 free pages, no credit card required.
Checkbox and radio button detection. Forms use checkboxes and radio buttons extensively — medical history checklists, coverage type selections, yes/no compliance questions, and multi-choice classification fields. Detecting whether a box is checked requires visual analysis, not text extraction. Respondents mark checkboxes inconsistently: some use check marks, others use X marks, filled circles, or heavy pen strokes that bleed outside the box. The AI needs to distinguish a checked box from an unchecked one regardless of marking style, and map the result to a clean boolean or category value in the spreadsheet output.
Handwritten field entries. Many forms arrive with handwritten responses in printed field areas. Patient names, addresses, dates, policy numbers, and narrative descriptions are written by hand in varying levels of legibility. Unlike pure handwriting OCR on blank pages, form handwriting recognition must also account for the printed field boundaries, pre-printed text that overlaps with handwritten entries, and the spatial relationship between the label and the response. The AI reads each handwritten entry in context — knowing that text next to "Date of Birth" is a date, not a name — to improve accuracy.
Multi-section form layouts. Complex forms divide information into labeled sections: personal information at the top, employment details in the middle, financial data in a table, and signatures at the bottom. Each section may use a different layout pattern — key-value pairs in one area, a grid table in another, and free-text fields in a third. Template-based tools require defining extraction zones for each section of each form variant. Layout-agnostic AI processes the entire form as a visual unit, identifying section boundaries and adapting its extraction approach to each section's structure automatically.
Conditional fields that may be blank. Forms often include conditional sections: "If yes, provide details below" or "Complete Section C only if you selected Option 2 above." When these conditional fields are left blank, the extraction system needs to recognize them as intentionally empty rather than failed extractions. It also needs to handle the downstream spreadsheet structure correctly — producing empty cells for skipped fields rather than shifting other data into the wrong columns. This conditional logic is invisible to simple text extraction tools.
Mixed form types across organizations. A single workflow often involves forms from many different sources. An insurance company receives claim forms from hundreds of healthcare providers, each using their own form layout. A government agency processes applications from businesses using different versions of the same form. A compliance team collects vendor questionnaires that each vendor has formatted differently. The extraction system must handle this variety without requiring a new template for every form variant it encounters.
AI-powered form extraction works by analyzing the visual layout of the entire form rather than searching for text in predetermined locations. The model identifies printed labels — "Policy Number," "Date of Service," "Patient Name" — and then locates the corresponding filled-in values, whether those values are typed text, handwritten entries, checked boxes, or selected radio buttons. This label-to-value mapping approach works across form designs without requiring templates, because the AI understands the spatial relationship between a label and its response field.
For handwritten entries, the AI combines character-level recognition with contextual understanding of the field type. A handwritten string in a "Phone Number" field is interpreted as digits and dashes, while the same ambiguous character shapes in a "City" field are interpreted as letters. This field-aware recognition produces significantly higher accuracy than generic handwriting OCR applied uniformly across the page. For forms with especially difficult handwriting, tools like handwritingocr.co specialize in maximizing recognition accuracy on cursive and compressed script.
Key-value pair extraction is the foundation of form-to-spreadsheet conversion. Each form field becomes a column header in the output spreadsheet, and each form processed becomes a row. When you batch-process 500 insurance claim forms, you get a 500-row Excel file where every column corresponds to a field on the form — claim number, patient name, provider, date of service, procedure codes, diagnosis codes, and billed amounts. This structured output is immediately usable for analysis, audit, and import into claims management systems.
Forms that contain table sections — itemized lists of procedures, line-item inventories, or multi-row schedules — require a hybrid approach. The AI identifies the table region within the form, extracts the tabular data with its row and column structure preserved, and embeds it alongside the key-value pair data from the rest of the form. For dedicated table extraction from PDFs, pdftableextraction.com focuses specifically on preserving complex table structures during conversion.
Insurance claim processing. Insurance companies and third-party administrators receive claim forms from healthcare providers, policyholders, and adjusters in paper and PDF format. Each claim form contains policyholder information, service dates, procedure and diagnosis codes, provider details, and billed amounts across multiple sections. AI extraction converts these forms into structured Excel rows for claims management systems, reducing the per-claim data entry time from 10–15 minutes to seconds. For organizations also processing W-2 tax forms, the same AI handles both form types without separate configuration.
Patient intake forms in healthcare. Medical offices, hospitals, and clinics collect patient intake forms that include demographics, insurance information, medical history checklists, current medications, allergies, and consent signatures. These forms are filled out by hand in waiting rooms and must be entered into the EHR system. AI extraction reads the handwritten entries and checked boxes, producing structured data that maps directly to EHR fields. All processing through Lido is HIPAA compliant and SOC 2 Type 2 certified, with automatic document deletion within 24 hours.
Government and regulatory filing. Government agencies process license applications, permit requests, tax filings, and regulatory compliance forms submitted by individuals and businesses. These forms follow standardized layouts but are filled out inconsistently — some typed, some handwritten, some a mix of both. AI extraction handles this variation and produces clean spreadsheet data for agency databases and case management systems. Batch processing is especially valuable during filing deadlines when thousands of forms arrive within a short window.
Vendor onboarding and compliance forms. Procurement and compliance teams collect vendor questionnaires, W-9 forms, insurance certificates, and onboarding packets from suppliers and contractors. Each vendor submits slightly different versions of these documents, and the data needs to be consolidated into a single tracking spreadsheet for compliance review. AI extraction pulls the relevant fields — company name, tax ID, insurance policy numbers, coverage amounts, expiration dates — into a unified Excel format regardless of how each vendor's form is laid out.
Upload insurance forms, intake forms, tax documents, or any filled-out form and get structured spreadsheet data back instantly
Yes. AI-powered extraction reads filled-out paper forms — insurance claims, patient intake forms, tax documents, and government filings — and converts the data into structured Excel rows and columns. The AI detects form fields, checkboxes, handwritten entries, and printed text without requiring templates or pre-configuration for each form type. Lido handles forms from any organization or layout with 50 free pages to start.
AI extraction detects checkbox and radio button fields visually, determining whether each option is checked, unchecked, or partially marked. The results appear in Excel as structured boolean values or the selected option text. This works for both printed checkboxes with handwritten marks and digitally filled PDF form fields, even when the check marks are inconsistent in style across different respondents.
Any structured or semi-structured form can be converted: insurance claim forms, patient intake and medical history forms, W-2s and tax documents, government applications, vendor onboarding packets, compliance questionnaires, and inspection checklists. The AI reads the form layout dynamically, so it works across industries and organizations without needing a separate template for each form variant.
Lido is SOC 2 Type 2 certified and HIPAA compliant, with AES-256 encryption at rest, TLS 1.2+ in transit, and automatic document deletion within 24 hours. A signed Business Associate Agreement is available for healthcare organizations processing patient intake forms, insurance claims, and clinical documents. Your forms are never used to train AI models.
50 free pages. All features included. No credit card required.