Tadiran · Document AI

The documents nobody wants to receive. We read them.

Zooz-tech built a Document AI system for Tadiran that understands documents the way a person does: handwriting next to printed text, receipts photographed on a phone at an angle and in poor lighting, tables, amounts and dates, in Hebrew and English. The output is structured data that goes straight into the business process.

Azure AI Document IntelligenceHandwriting recognitionMultilingual OCROutput to ERP, Excel and API

From a hard-to-read document to structured data: the processing layers and the ready-to-use output

From image to data

What this invoice is hiding

Every field is handwritten, some in Hebrew and some in English, and some inside a printed table. The system identifies each field, understands what it is and returns it as structured data that can go into the ERP.

An illustration of the output, based on the fields visible in the invoice.

Document number: 2476
Date: 04.05.2026
Item: TADIRAN SOLO 140 INV NG
Serial number: 4V8115008504
Total incl. VAT: 2,600.00 ₪
Payment: Bit · 12.05.2026

Not a demo on a perfect PDF

These are the documents that come in from the field

An invoice handwritten in a receipt book, with a model number, a serial number and an amount. A receipt photographed in a hurry, skewed, with tiny text and mixed languages. Standard OCR breaks exactly here. Our system starts here.

Handwriting and print in the same documentPhoto taken at an anglePoor lightingSmall textHebrew and English

Handwritten invoice: model details, serial number and amount, with identifying details blurred — A handwritten invoice. Identifying details have been blurred.

A receipt photographed in poor lighting and at an angle — A photographed receipt: angle, shadow and tiny text

The difference between OCR and understanding

Recognizing a number is easy. Understanding what it is, that is the work.

When 282.00 appears, the system needs to understand whether it is a total, an item price, VAT or a balance. A handwritten date needs to be linked to its field, not left as a string of digits on the page. That is the difference between OCR and AI Document Understanding.

How it works

Five steps, from image to business process

1
The document comes in
A phone photo, a scan, a PDF or an email.
2
Enhancement and decoding
Straightening, contrast, and recognition of printed text and handwriting.
3
Understanding structure and context
Tables, fields and what each value means.
4
Extraction and validation
Amounts, dates, identifiers and items, with a confidence level for each field.
5
Into the process
Structured JSON for the ERP, for Excel or through an API.

The really hard cases

Eight things that break standard OCR

Handwriting

Even next to printed text, in the same document.

Hebrew, English and more

Mixed languages and writing directions on a single line.

Photos at an angle

A document photographed by hand, not scanned.

Poor lighting and shadows

The technician's phone, not an office scanner.

Low-quality scans

Faded paper, faxes and copies of copies.

Tables and fields

Line items, columns and totals.

Numbers and identifiers

Amounts, dates, serial numbers and model numbers.

Small text

Till receipts and tiny print.

Results

What it delivers in the field

Very fast response time

Even on complex, multi-page documents.

Results on the hardest documents

Skewed photos, faded documents, handwriting and small text.

Ready-to-use output

Structured data for the ERP, for Excel or through an API.

The same capability, across our systems

Document AI in other projects

A live claim caseHundreds of scanned documents, and a vision model that fills in where legibility is poor AICare · Israel Aerospace IndustriesOpening a service request from a photo of an ID nameplate, with P/N and S/N recognition Gil CarInvoices as PDF, image or email, straight into SAP MoroccanoilA Packing List email becomes a set of export documents

Send us your hardest document

We will send it back to you as structured data.

Send a document for testing