A Certificate of Analysis (COA) is one of the most important documents in a manufacturing and quality-control environment.
It contains critical information about a product, material, batch or lot: test results, specifications, supplier information, signatures, remarks and other quality parameters. Yet COAs rarely arrive in a standardized format.
One supplier may send a structured digital PDF. Another may provide a scanned certificate with multiple tables. Some documents may contain handwritten signatures, notes, reference documents or several sets of test results.
This is where Deep Learning for document processing can make a significant difference.
Instead of simply reading text from a document, deep-learning-based Intelligent Document Processing (IDP) can help a system understand the structure, context and relationships within complex quality documents.
Why Traditional OCR Isn’t Enough for COA Processing
Optical Character Recognition (OCR) has been used for years to convert scanned documents into machine-readable text.
But a COA is more than a collection of words and numbers.
Consider a typical certificate containing:
- Supplier information
- Certificate number
- Material or product description
- Batch or heat number
- Multiple test tables
- Chemical composition
- Mechanical properties
- Specifications
- Notes and remarks
- Reference standards
- Digital or handwritten signatures
Basic OCR may successfully recognize individual characters.
The bigger challenge is determining:
What does each piece of information mean, and where does it belong?
For example, the value 0.18 means very little by itself.
A deep-learning system needs to understand whether it represents:
- Carbon percentage
- A dimensional measurement
- A test result
- A specification limit
- Or something else entirely.
That is the difference between text recognition and document understanding.
What Does Deep Learning Bring to COA Automation?
Deep learning enables document-processing systems to identify patterns and relationships across large volumes of documents.
Rather than relying entirely on fixed templates, the system can learn how information is typically presented and progressively improve its ability to process variations.
For COA processing, this can be particularly useful for identifying several different types of information.
1. Supplier Detection
COAs can arrive from hundreds of suppliers, each using its own format.
Supplier detection helps identify the source document and determine how its information should be interpreted.
This reduces the need to maintain a completely separate manual process for every supplier.
The result is a more scalable approach to multi-supplier COA automation.
2. Multiple Table Detection
Tables are often the heart of a COA.
A single certificate may contain multiple tables covering:
- Chemical composition
- Mechanical properties
- Physical properties
- Product specifications
- Test results
- Acceptance criteria
A deep-learning-based system can identify different tables and understand their boundaries and structure.
This is particularly important because extracting numbers without preserving their row-column relationships can lead to incorrect quality records.
3. Multiple Test Detection
A COA may contain several tests performed on the same material or batch.
The system needs to distinguish between different tests and associate the corresponding values with the right parameter.
For example:
Test → Parameter → Result → Unit → Specification → Status
Maintaining these relationships is essential for reliable downstream validation.
4. Understanding Notes and Remarks
Important information isn’t always contained inside neatly structured tables.
Manufacturers and suppliers frequently add:
- Notes
- Remarks
- Special instructions
- Exceptions
- Processing information
- Additional quality observations
Deep-learning-powered document understanding can help identify this contextual information rather than treating it as irrelevant text.
This becomes especially valuable when the information affects how a quality record should be interpreted.
5. Digital and Handwritten Signatures
A COA may include a digital signature, a scanned signature or a handwritten approval.
Recognizing these elements can help determine whether the certificate contains the expected approval information.
For organizations concerned with quality compliance and audit readiness, knowing that a certificate has been reviewed or signed can be an important part of the overall document record.
6. Reference Document Detection
COAs sometimes refer to other documents or standards.
These references can provide important context about:
- Testing procedures
- Specifications
- Quality requirements
- Material standards
- Customer requirements
Deep-learning-based document analysis can help identify references and connect them with the appropriate information within the certificate.
This moves COA processing closer to context-aware document intelligence.
Deep Learning vs. Template-Based Extraction
Traditional document automation often depends heavily on predefined templates.
This can work well when every document follows the same structure.
But real-world COAs are rarely that predictable.
| Capability | Template-Based OCR | Deep Learning-Based IDP |
|---|---|---|
| Basic text extraction | ✓ | ✓ |
| Fixed document formats | ✓ | ✓ |
| Variable layouts | Limited | ✓ |
| Multiple tables | Limited | ✓ |
| Context understanding | Limited | ✓ |
| Supplier variations | Requires configuration | Better suited |
| Notes & remarks | Limited | ✓ |
| Signature detection | Limited | ✓ |
| Multiple test structures | Limited | ✓ |
| Continuous learning | Limited | ✓ |
From Extraction to Understanding
This distinction is becoming increasingly important in Intelligent Document Processing.
A modern COA automation workflow can be thought of as:
Extract → Understand → Validate → Verify → Integrate → Trace
Extract
Capture text, numbers, tables and other document elements.
Understand
Determine what each element represents and how it relates to other information.
Validate
Compare extracted results against specifications, rules or reference data.
Verify
Route uncertain or exceptional information for human review.
Integrate
Send structured information into ERP, LIMS, QMS or other business systems.
Trace
Maintain the connection between the original certificate and the resulting quality record.
This is where the value of deep learning becomes much greater than simply improving OCR accuracy.
Why This Matters for Manufacturing
For organizations processing thousands of certificates, manual COA processing can create several challenges.
Manual data entry
Quality teams may spend significant time transferring information from certificates into spreadsheets or enterprise systems.
Inconsistent formats
Every supplier can potentially introduce a different document structure.
Data-entry errors
A single incorrect value can potentially affect quality decisions, downstream processing or customer documentation.
Slow validation
Teams may need to manually compare test results against specifications.
Limited traceability
When information is manually copied into another system, maintaining a clear link to the original certificate can become difficult.
Deep-learning-powered automation addresses these challenges by turning complex documents into structured, usable quality data.
Deep Learning Is Particularly Valuable for Complex COAs
The real test for an AI document-processing system isn’t a clean, standardized one-page document.
It is the messy, real-world certificate.
A document containing:
Multiple tables + different suppliers + test results + notes + signatures + reference information
requires considerably more than conventional OCR.
This is the type of environment where deep learning can provide meaningful value.
Star Software’s approach to COA processing reflects this broader shift toward document intelligence, with capabilities designed to handle elements such as supplier detection, multiple-table detection, multiple-test detection, notes and remarks, reference documents, and digital or handwritten signatures.
The objective is not merely to digitize the certificate.
It is to understand the certificate and convert it into reliable business data.
What Should Companies Look for in Deep-Learning COA Automation?
When evaluating a COA automation solution, organizations should look beyond the phrase “AI-powered OCR.”
Ask:
- Can it handle different supplier formats?
- Can it identify multiple tables on the same certificate?
- Can it distinguish different tests and their corresponding results?
- Can it understand notes and remarks?
- Can it detect signatures and approval information?
- Can it validate extracted values against specifications?
- Can users review uncertain results?
- Can the extracted information be integrated with ERP, LIMS or QMS?
- Can every quality record be traced back to the source certificate?
These questions reveal whether the solution is genuinely providing document intelligence or simply performing OCR.
The Future of COA Automation Is Document Understanding
COA automation is moving beyond simple scanning and data extraction.
The next generation of systems will increasingly combine:
OCR + Computer Vision + Deep Learning + Business Rules + Workflow Automation
to understand complex quality documents.
For manufacturers, this means a COA can become more than a static PDF stored in a folder.
It can become a structured, validated and traceable quality record that feeds directly into the organization’s digital processes.
And that is perhaps the most important shift:
The future of COA automation isn’t about teaching computers to read documents. It’s about teaching them to understand what those documents mean.
