How to Extract Data from Handwritten Documents with AI

Many business documents still contain handwriting. Signed delivery receipts, annotated invoices, handwritten driver tickets, dock tallies, field inspection forms—they arrive as scans, photos, or PDFs mixing printed and handwritten text. Traditional OCR tools fail on these. Someone ends up transcribing handwritten entries by hand, field by field, into a spreadsheet. It’s slow, error-prone, and scales terribly.

Why traditional OCR fails on handwritten documents

Traditional OCR data extraction was built for typed, printed text in standard fonts. It matches character shapes against a known library of letterforms. Handwriting breaks that model completely.

1. Every person’s handwriting is different. Letter shapes, spacing, slant, and ink quality all vary by writer. A “7” that looks like a “1,” a “4” that looks like a “9”—these are judgment calls that character-matching OCR can’t make.
2. Most documents mix printed and handwritten text. A typed form with handwritten entries. A printed invoice with handwritten delivery notes. A bill of lading with pre-printed fields filled in by hand. The OCR engine has to handle both on the same page—and know which text belongs to which field.
3. Template-based tools break entirely. Handwritten text doesn’t appear in fixed positions. A driver writing a total in the margin, a receiver scrawling a count next to a pre-printed field—none of this conforms to a rigid template. Tools that rely on coordinates will miss handwritten entries or extract the wrong values.

How to extract data from handwritten documents with Lido

1. Step 1: Upload or forward the document. Scan, phone photo, PDF—Lido accepts any format. For field documents captured on-site, a phone photo works. No need to pre-process, crop, or convert.
2. Step 2: Define the fields you need in plain English. “Driver name,” “delivery date,” “total weight,” “signature present”—describe what you need, not where it appears on the page. No coordinates or templates required.
3. Step 3: Lido’s AI reads the full document. Vision models interpret both printed and handwritten text, understanding context rather than matching characters. A handwritten number next to a printed “Total” label is the total amount, regardless of penmanship.
4. Step 4: Review and export. Lido flags low-confidence fields—typically where handwriting is ambiguous—for human review rather than silently guessing. Clean data exports to Excel, CSV, Google Sheets, or JSON.

Tip: Neat block handwriting extracts with high accuracy. Hurried cursive is harder—expect to review more flagged fields. For best results, scan at 300 DPI or higher.

Handwritten document extraction results

Kei Concepts (restaurant group, 13 locations) processes handwritten Vietnamese-language restaurant documents—invoices, receipts, delivery slips—that no other OCR tool could handle. Mixed printed Vietnamese and handwritten entries. Lido reads them accurately.

Disney Trucking extracts data from handwritten driver tickets. Every load generates a handwritten ticket with weights, times, and driver information. Previously, each ticket required manual transcription. Now automated with Lido.

Logistics companies processing handwritten bills of lading—stamped, annotated, and covered in carrier-specific notations—use Lido as document capture software to extract shipper details, weights, and freight charges without templates. Lido handles the variation because it reads context, not coordinates.