Technicians fill out forms in the field. You photograph them and get structured data back—handwritten notes, checkboxes, and all. No templates. No per-form setup.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. Any file format works—PDF, JPEG, PNG, TIFF, or digital documents.
The AI identifies fields by context and meaning, not fixed coordinates. Names, dates, amounts, and custom fields are extracted automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API for direct integration into your systems.
“Our HVAC techs fill out paper work orders on every service call. The office used to spend four hours a day typing them into our system. Now they photograph each form and we have the data in a spreadsheet by end of day.”
“Facility inspections generate hundreds of checklist forms per quarter. The checkbox detection is what sold us—it correctly reads checked versus unchecked boxes on our compliance forms and flags anything ambiguous.”
“We still use carbon copy work orders in the field. The technician’s copy is always faded and hard to read. This tool pulls the data from even the worst carbon copies without issues.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
Work order OCR converts paper-based field service documents—maintenance forms, inspection checklists, service call tickets—into structured digital data. In trades like HVAC, plumbing, electrical, and facility management, technicians still fill out paper work orders on job sites where mobile apps are impractical or where multi-part carbon copy forms are required for customer sign-off. The data on those forms needs to reach dispatch, billing, and CMMS systems, and without OCR that means someone in the office retypes every field by hand.
Work orders present unique OCR challenges compared to invoices or financial documents. They contain a mix of printed form structure and handwritten entries. Technicians write in field conditions—on clipboards, in poor lighting, sometimes with gloved hands—producing handwriting that varies dramatically in legibility. Forms include checkboxes for inspection items, signature fields, and free-text notes describing work performed. Traditional OCR tools designed for printed text fail on these mixed-format documents.
AI-powered work order OCR addresses these challenges by using the printed form structure as context for interpreting handwritten content. The AI knows that a handwritten entry next to a label reading “Equipment ID” is an equipment identifier, which constrains the recognition to alphanumeric patterns typical of asset tags. Checkboxes are detected as checked or unchecked based on visual analysis rather than character recognition. Lido applies this contextual approach to extract structured data from any work order format without requiring pre-built templates.
For facility management and field service companies, the operational benefit extends beyond data entry savings. Digitized work order data enables analysis of service patterns, technician productivity, equipment failure rates, and compliance trends—insights that remain invisible when work orders sit in filing cabinets or are typed into spreadsheets weeks after the work was completed.
AI-powered work order OCR processes handwritten text with increasing accuracy, especially when combined with printed form structure. The AI uses printed labels and checkbox positions to provide context for handwritten entries. Lido flags handwritten fields with confidence scores so dispatchers can quickly verify uncertain entries.
Work order OCR handles preventive maintenance forms, corrective maintenance reports, service call tickets, inspection checklists, equipment logs, and multi-part carbon copy forms across trades including HVAC, plumbing, electrical, and facility management.
The AI identifies checkbox regions and determines whether each box is checked, unchecked, or ambiguous. Checked boxes are extracted as true values and unchecked as false. Ambiguous marks are flagged with lower confidence for human review.
Yes. Multi-part work orders where the technician’s copy is a faded carbon duplicate are common in field service. The AI handles degraded print quality by using contextual recognition rather than relying on character clarity alone. Upload a scan or photo of any copy.
Lido offers 50 free pages to test work order OCR. The Standard plan starts at $29 per month for 100 pages. Scale plans for facility management teams start at $7,000 per year for up to 42,000 pages. Enterprise pricing is available for CMMS integration or compliance requirements.
Start free with 50 pages. Upgrade when you’re ready.
Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine