Chain Digitise and Extract | Sarvam API Docs

For high-quality scans, a single Extract project pulls fields directly from the source. But for low-quality scans, historical documents, handwritten forms, or dense multilingual pages, chaining Digitise → Extract delivers noticeably higher accuracy.

The pattern: first digitise the document into clean structured text, then run an Extract Config over that clean text.

When chaining is worth it

Blurry or skewed scans

Digitise applies layout-aware cleanup before the Extract project ever sees the content.

Handwritten documents

Handwriting benefits from the Vision model’s full attention on OCR, not field-finding.

Multilingual pages

Digitise handles Indic scripts; Extract works over the resulting clean text.

Complex layouts

Multi-column pages, sidebars, and footnotes get flattened into natural reading order first.

For clean, printed English or single-language invoices, skip chaining. A direct Extract project is faster and just as accurate.

The two-Config pattern

You’ll build two Configs on the dashboard and run them as two Projects in sequence:

A Digitise Config

Cleans the source document into structured text.

An Extract Config

Pulls your target fields from that clean text.

Step 1: Build the Digitise Config

Follow Digitise a document to build a Digitise Config. Set:

Document format

Handwritten for manuscripts and forms, Printed for typeset documents.

Document language

The primary language of the source: English, Hindi, Tamil, and other supported languages.

Name it something clear like Scan Cleanup so teammates browsing configs know what it does. Save it.

Step 2: Build the Extract Config

Follow Extract structured fields to build an Extract Config. Copy this into Describe what to extract, then edit for your document type:

Extract the invoice number, invoice date, vendor name, total amount, and GST ID from the clean invoice text.

Leave the output format on Generate from prompt. Name it Invoice From Text and save.

Add a hint to the Extract Config’s description that reminds teammates it consumes Digitise output:

Inputs are cleaned-up text from the Scan Cleanup Config.

Step 3: Chain the two Projects

Run the Digitise project

Open the Scan Cleanup Config, upload the messy scan, and click Digitise. Wait for the status to reach Completed.

Copy or export the digitised text

Open the completed Digitise project. Either click Download to save the structured text, or copy the digitised text directly from the Digitised text tab.

Run the Extract project

Open the Invoice From Text Config. When it prompts for a document, paste the digitised text (or upload the exported text file). Give the project a name and click Extract.

Review the extracted fields

Extracted values appear as a field-by-field table. Correct any that look off inline, then download as JSON, CSV, or Excel.

Both projects show up in Workspace → Projects. To keep them associated, use a consistent project-name prefix, e.g. Acme-Invoice-Jun 4: Digitise and Acme-Invoice-Jun 4: Extract.

Compare accuracy before committing

Run the same 20 test documents through both approaches (direct Extract vs Digitise then Extract) and compare results inside the dashboard. Two things to check:

Field-level accuracy

How many fields match your ground truth on each approach?

Time and credits

Chaining doubles the credit spend and roughly doubles processing time.

If chained-mode accuracy is meaningfully higher (say, 5%+), the extra step is worth it. If not, save the credits and stick with direct Extract.

Trade-offs

Higher accuracy on messy docs

Digitise cleans up layout, orientation, and OCR noise before extraction ever runs.

Two projects per document

Roughly 2x credits and 2x processing time vs a direct Extract project.

For production, route documents by scan quality. High-quality scans go direct, low-quality scans go through the chain. You can automate this by tagging incoming documents on your side before uploading.

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