An accounts employee opens an invoice, copies the supplier name and total, then types every line item into another system. HR repeats the process for application forms and joining documents. The work is repetitive, but errors still need careful review. An AI document parser can help move information into structured records while giving the team a clear way to check uncertain fields.
The problem is moving information, not just reading text
Documents contain information in layouts designed for people, while software needs named fields and consistent data types. Reading a scanned page does not automatically tell an accounting system which number is the invoice total or which date is the payment due date. Even a readable PDF may contain several tables, notes and amounts that need to be distinguished before export.
List the target fields for each document type. An invoice might require supplier, invoice reference, date, line items and totals. An application form will need a different schema. This field map defines what a successful extraction looks like and prevents an attractive text transcript from being mistaken for a usable business record.
Understand where OCR ends and document processing begins
Optical character recognition turns scanned or image-based text into machine-readable text. Intelligent Document Processing adds document classification, field and table extraction, validation and structured output. These steps help connect a document with the receiving application's requirements. The distinction matters when your goal is to create a reviewed invoice record rather than simply search the words in an archive.
How Pedinno Document Parser handles the pipeline
Pedinno Document Parser supports individual or batch intake, document type detection, OCR text extraction, field and table extraction, configured validation rules and exception review. Reviewed data can be exported as structured JSON or database records and integrated with business software through scoped APIs. Supported categories include invoices, purchase orders, identity documents, forms, bank statements and HR or educational records.
Validate important values before they move downstream
Define checks for the fields that matter to your process. For an illustrative invoice workflow, that might include a required invoice reference, a recognisable supplier and a total that can be reviewed against line items. Agree on how duplicates and missing fields are handled as part of the integration design. Pedinno flags low-confidence or invalid fields for review, so reviewers can focus on exceptions while still checking critical values before use.
- Collect representative layouts, including difficult scans.
- Define required fields and expected output types.
- Agree on validation rules and exception ownership.
- Test structured output against the receiving system.
- Keep a review step for high-impact values and uncertain extraction.
Choose templates and integration scope deliberately
Pedinno supports custom document templates as well as flexible extraction approaches. A repeated supplier format may benefit from a template, while a diverse intake needs broader testing. Identify where reviewed output should go and which fields require additional mapping. Do not assume that every document can be posted directly into an existing ERP without an integration step. Start with one receiving workflow and make its acceptance rules explicit.
Run a sample-based pilot before promising accuracy
Use anonymised samples that represent the real mix of formats, image quality and tables. Compare extracted fields with a checked reference set and measure corrections, exception volume and reviewer time. Include pages with missing values and ambiguous totals. Extraction quality depends on document quality and configuration, so results from one clean PDF do not establish performance across an entire archive. Expand only after reviewers trust the handling of difficult documents.
Frequently asked questions
Can Pedinno extract invoice line-item tables?
Table extraction is part of Document Parser. Test your actual invoice layouts and field mappings during a sample-based pilot.
Does AI extraction eliminate human review?
No. The workflow flags uncertain or invalid fields for review, and important data should be checked before it is used downstream.
Can the parser export structured data?
Reviewed output can be exported as structured JSON or database records. Integration with your receiving application is scoped to its interfaces and requirements.
See Document Parser in action
A multi-document OCR and Intelligent Document Processing system that extracts, classifies, validates, and structures information from business documents.
Bring anonymised examples of your common and difficult documents, plus the fields your receiving system requires.
Explore Document Parser