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OCR turns a page into text; document processing turns mixed documents into checked, usable data.
OCR is enough for searchable text. If someone still retypes numbers from the output into another system, you need intelligent document processing.
OCR (optical character recognition) reads the characters on a scanned page or photo and gives you text, sometimes with positions. It does not know that 33ABCDE1234F1Z5 is a GSTIN, that the number beside Total is the invoice total, or that page 3 belongs to a different document.
Intelligent document processing (IDP) wraps OCR in everything else a business needs: sorting documents by type, extracting named fields, checking them against rules and your own records, sending doubtful cases to a person, and exporting clean data to Tally, an ERP or a CRM.
In the OCR vs intelligent document processing choice, plain OCR is enough when you only need searchable text. If a person still has to read the OCR output and type numbers into another system, you need IDP. Our OCR and document intelligence service builds the second kind.
| Compared on | OCR | Intelligent document processing |
|---|---|---|
| Output | Text, sometimes with word positions and confidence | Named fields in a fixed schema: vendor, GSTIN, invoice number, date, line items, taxes, total |
| Document types | Any page, treated the same | Classifies each document first: invoice, credit note, bank statement, PAN, Aadhaar, form |
| Layouts | Layout-agnostic text | Handles varying layouts from many suppliers without a template per supplier |
| Validation | None | Checks totals against line items, GST arithmetic, GSTIN format, dates, duplicates |
| Human review | Not built in | A review queue for low-confidence or failed checks |
| Output destination | A text file or searchable PDF | Tally, an ERP, accounting software, a CRM or a spreadsheet |
| Typical tools | Tesseract, cloud OCR services | OCR plus layout models or language models, rules, a review screen and integrations |
| Best for | Archives, search, simple fixed forms | Accounts payable, KYC onboarding, claims, application forms |
Searchable PDFs from old scans are cheap and enough when people only need to find pages.
If you have ten years of scanned files and want to find every letter that mentions a customer, OCR into searchable PDFs is cheap, fast and enough. Nobody needs fields; they need to find the page.
Zonal OCR and a few rules can handle one form you designed yourself.
If every document is the same form you designed, with boxes in the same place, zonal OCR plus a few rules can be simpler than a full IDP system.
The same fields from a hundred invoice layouts, with unsure ones flagged instead of failing silently.
A business with a hundred suppliers receives a hundred invoice layouts, some as PDFs, some as phone photos of thermal-paper bills. IDP extracts the same fields from all of them and flags the ones it is unsure about, instead of failing silently.
GSTIN structure, tax arithmetic, place of supply and duplicates checked before entries reach the books.
A GSTIN is 15 characters: a two-digit state code, the PAN, an entity number, a default letter and a check character. IDP can verify that structure, check the state code against the place of supply, confirm CGST plus SGST or IGST adds up, and catch a duplicate invoice number before it reaches your books.
Only low-confidence fields go to a reviewer, shown beside the page image.
For KYC documents, insurance claims or medical forms, you want a person to confirm low-confidence fields. IDP routes only those fields to a reviewer with the image beside them, rather than asking someone to re-check every page.
Files arrive by email, WhatsApp, upload or folder, and the original is stored untouched.
The PDF or photo arrives by email, WhatsApp, upload or a shared folder. The pipeline stores the original untouched.
Digital PDFs give text directly; scans and photos are cleaned up and read by OCR.
Digital PDFs give their text directly; scans and photos go through OCR, with deskewing and contrast correction for phone pictures.
A model identifies the document type and fills a fixed schema, including line items.
A model decides the document type and pulls out the fields in a fixed schema, including line items as a table.
Rules check arithmetic, tax splits, GSTINs, dates, duplicates and the vendor name.
Rules check arithmetic, tax splits, GSTIN structure, dates and duplicates, and match the vendor against your master list.
Failed or low-confidence fields go to a person, shown beside the highlighted page area.
Anything that failed a check or came back with low confidence goes to a person, who sees the field next to the highlighted area of the page.
Approved data goes to Tally or your ERP with a link to the original.
Approved data is written to Tally, the ERP or accounting software, with a link back to the original document for audit.
OCR is cheap per page; IDP adds models, rules, integrations and reviewer time. Measure cost per correctly posted document, not per page.
OCR is cheap per page. Open-source Tesseract has no licence fee and runs on your own server. Cloud OCR and document services such as AWS Textract, Google Document AI and Azure AI Document Intelligence bill per page, with higher per-page rates for features such as table, form or custom field extraction.
IDP adds layers on top: a language model or layout model for extraction (billed per token or per page if cloud-hosted, or a fixed hardware cost if run privately), the engineering to build validation rules and integrations, a review screen, and most importantly the time reviewers spend on flagged documents. That last cost is the one to design for. A pipeline that sends every document to review saves little; one that confidently posts wrong numbers is worse than none.
So the right measure is not cost per page but cost per correctly posted document, including review time.
We build IDP with human review always included, and prefer private models for Aadhaar, PAN, medical forms and client financials.
We build IDP for documents that end up as entries in another system, and we always include human review. One of our anonymous client builds is an OCR pipeline for invoices, IDs and forms where a person reviews flagged fields before anything is posted. We do not hide that step; it is what makes the output trustworthy.
For sensitive documents such as Aadhaar, PAN, medical forms or client financials, we prefer running extraction on a private model so images do not leave your infrastructure, which also simplifies DPDP Act 2023 questions.
If your goal is getting purchase bills and expenses into your books, look at Fint, where an AI agent drafts expenses from bills and a person confirms them, or our GST and accounting automation service for a custom pipeline into Tally. For the definition, see intelligent document processing.
Modern OCR and vision models read clear handwriting reasonably well and struggle with rushed or cramped writing. For handwritten forms we plan for a higher share of fields going to human review.
Not with current IDP. Language and layout models extract fields from layouts they have not seen before. Templates still help for one very high-volume document where you want maximum consistency.
Yes. We build integrations with Tally, so approved invoices and expenses can be pushed as vouchers with the original document linked. We recommend a review step before posting, at least until accuracy on your documents has been measured.
It depends on your documents: clean digital PDFs extract very well, crumpled phone photos of thermal bills less so. We measure accuracy on a sample of your own documents before go-live rather than quote a general figure.
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