Signature verification for any signed document
Find the signature.
Lift it out.
Compare it with your records.
Chequedb checks whether a document is signed, lifts the signature out of the image, and compares it with the samples you already hold — cheques, forms, contracts, delivery notes. When it isn’t sure, a person decides, with the document, the sample and the reason in front of them.
- Spot blank, missing and covered signatures before anything else.
- Works on cheques, forms and other scanned documents — from a scanner or a phone.
- Keeps the result, the sample version and the reviewer’s decision together.
Every document shown on this page is a synthetic illustration. No customer images are used.
A scan or a phone photo of the document is checked for sharpness and glare before anything else happens.
A drawn example on a synthetic cheque. The same five steps apply to any signed document — a form, a contract or a delivery note. No live model runs here and no similarity score is shown.
How it works
Detection, localisation and comparison, in plain words.
Five steps turn an image of a signed document into a decision you can explain later. The technical names for those steps live in the technical reference; the rest of this page uses ordinary language.
- 1
Take the picture
A scan or a phone photo. The image is checked for sharpness, glare and framing first.
- 2
Find the signature
A quick check of whether the document is signed at all, and roughly where the signature sits.
- 3
Lift it out
The signature is cut out of the image at full quality, with a note of exactly where it came from.
- 4
Compare
It is set beside the samples you already have on file for that account, customer or signer.
- 5
Record the decision
The result, the sample version and the reviewer’s reason are stored together.
Where it applies
Cheques, forms, contracts — anything with a signature.
Signature detection, localisation and comparison are not cheque-specific. If a document carries a handwritten signature and you hold authorised samples for it, the same five steps apply. Cheques are simply the case we know best.
Cheques and deposit slips
The drawer’s signature at the foot of the cheque, plus endorsements on the back where you need them.
Account-opening and loan forms
Applicant, co-applicant and guarantor signature blocks, often spread across several pages.
Contracts and agreements
Signature blocks on the final page, and initials in the margins of amended clauses.
Delivery and goods-received notes
Proof-of-delivery signatures that need checking against a known signatory before payment.
Insurance claim and settlement forms
Claimant and assessor signatures on documents that arrive by post, email or as phone photos.
Mandates and payroll forms
Standing-order mandates, letters of authority and payroll change forms with an authorised signatory.
What changes from one document type to the next
Where the signature is expected. A cheque has a defined signature block; a contract may have one at the end and initials in the margins. Expected locations are configured per document type.
How many signers. Some forms need one signature, some need two, and joint accounts need rules for both. The count is part of the configuration, not an afterthought.
Which samples apply. Authorised samples belong to a person or an account, not to a document. The same reference set can serve every document that person signs.
What the document’s own rules require. Cheque law, contract execution requirements and your internal policy all differ. The check supports those rules; it does not replace them.
Step 2 · Detection
Is it signed — and can we read it?
Before any comparison, two simpler questions come first: is there a signature, and is the image clear enough to use? There are three possible answers, and each one leads somewhere different.
Signed
A signature is there and looks usable. It gets lifted out of the image and compared with the samples you hold.
Next: compare it with the samples on file
Nothing there
The signature space is empty. Nothing gets scored — the document is marked as unsigned and sent to a person.
Next: flag it as unsigned and route it
Hard to read
A stamp, glare, blur or a clipped edge is in the way. A signature may well be there, but we say so instead of guessing.
Next: ask for a clearer image, or send it to review
There is a signature here
The outlined area contains a signature-like mark. It can be lifted out of the image and compared with the samples on file.
Next step: check the image is sharp enough, then compare
Drawn examples explain the workflow; they are not live model predictions or performance measurements.
Finding a signature does not tell you who signed it. Printed names, stamps, initials and handwritten notes can all complicate the picture, so these cases belong in your own evaluation set — whatever document they appear on.
Step 3 · Localisation
Getting the signature
out cleanly.
Cutting the signature out of the document makes it easier to compare, and easier for a person to check. Engineers call this signature localisation. What matters in practice is that the crop is clean, correctly placed, and still tied to the page it came from.
Keep it tied to the document. A crop is easier to compare, but a reviewer needs the whole page to see nearby stamps, print and layout.
Don’t clip the flourish. A crop that cuts off the end of a stroke invents a difference that isn’t really there.
Expect more than one box. Joint accounts and some layouts carry several signature areas. Each expected area is handled separately.
02 / RETAIN THE CROP
x: 365 · y: 174
width: 222 · height: 90
Example pixel coordinates, relative to the source image.
Step 4 · Comparison
Does it match what
you already have on file?
Comparison looks at the shape of the writing, the spacing between strokes and where they sit — measured against the signed samples you already hold. It is a measurement, not a verdict.
Compare the visible geometry
Authorised reference
Reference sample
From this document
Candidate sample
Similar shapes can still contain natural variation
Look at the opening stroke, proportions, spacing, and terminal flourish. Small changes are normal; evaluate against several authorised samples and the image quality.
Synthetic drawings. No live comparison is performed and no similarity score is implied. The step is identical whether the page is a cheque, a form or a contract.
Use more than one sample
Nobody signs the same way twice. Several authorised samples describe real variation far better than one perfect specimen.
A bad photo is not a bad signature
Glare, blur and low resolution are image problems. They are reported as image problems, not as a mismatch.
You set the bar
How close is close enough depends on your documents, your risk appetite and how much review capacity you actually have.
The awkward cases
The documents that need a human eye.
A useful pilot includes the difficult documents, not only the clean ones. These are the cases that decide whether signature checking helps your operation or gets in its way.
Glare and shadows
An overhead light or a phone-camera flash can wash out the strokes and make a genuine signature look faint.
Too small or too soft
Low-resolution scans lose the stroke detail a comparison depends on. Resolution is worth checking before anything else.
Something covering it
Stamps, annotations and printed text can sit over the signature area and hide part of the writing.
More than one signer
Joint accounts and multi-party forms need a sample set for each person, plus a rule for how many signatures are expected.
A signature that drifted
People’s signatures change over the years. Samples from a decade ago describe a different hand.
Initials and amendments
Initials beside an amendment are not a full signature, and should not be measured as one.
None of these are reasons to skip signature checking. They are the cases to put in front of a system before you trust it with the easy ones.
What a photo
can and cannot tell you.
An image of a document contains visible pixels. It can support analysis of shape, texture, overlap, proportions, and image quality. It does not record the original pen pressure, writing speed, or stroke timing.
That is why signature checking works best as one part of the decision. For cheque fraud detection, the signature result is combined with the other transaction checks, and the final call is recorded with a name against it.
Step 5 · Review
Nothing is approved by a score alone.
Anything unclear, unmatched or simply unusual goes to a person, with everything they need on one screen. That is the part most teams care about most, because it is the part they have to defend later.
What the reviewer sees
The whole document, the signature crop, the sample it was compared with, and the reason it was sent for review.
What gets recorded
The result, the sample version, the reviewer’s action, the reason and the time — stored with the image it came from.
What never happens
A missing signature, a blurred image or a failed check is never quietly turned into a pass.
Technical reference
The same five steps, in system terms.
For engineering, risk and operations teams. Everything above is the operational summary; the definitions, data handling and measurement detail for a build or a procurement review are below.
Detection, localisation, comparison
Detection establishes whether a signature candidate is present in the document image, and whether the region is usable. It is a presence check before any comparison.
Localisation returns the region of the source image that contains the candidate — the bounding box, the image dimensions it refers to, and any rotation, resizing or deskew applied first.
Comparison evaluates the candidate crop against an authorised reference set and returns quality flags and comparison signals. Detection on its own does not establish who signed a document.
Coordinates, crops and image handling
Retain the box, image dimensions, and any rotation or resizing applied before extraction so a crop always maps back to its source image.
Preserve the full page alongside the crop. A crop helps comparison; the full image lets a reviewer inspect nearby stamps, text and layout.
Avoid cutting off a flourish or including unrelated handwriting, and handle multiple signature fields explicitly rather than assuming one box per document.
Reference sets, thresholds and calibration
Compare against authorised samples for the correct person, account or signer, and keep the version and provenance of each reference set.
Thresholds belong to you. Calibrate them on labelled data from your own capture channels, and treat a quality failure differently from a comparison result.
A similarity score alone is neither proof of authenticity nor a calibrated probability, so record the threshold and reference version alongside every result.
Failure handling
Missing signature, unavailable reference, unusable image, and processing failure must not silently become a successful match.
Each condition is returned as its own outcome with a reason, so the workflow can route it to recapture or to a reviewer instead of producing a misleading score.
Retain the failures in your evaluation set as well. Reviewing only the clean successful examples hides exactly the cases that matter.
Integration
Evidence that travels with the result.
Use the Chequedb API integration to connect image processing with your deposit, operations or review system. Agree the exact request and response contract for your deployment.
Input. Document image or signature crop, document type, transaction context, and the authorised reference source when comparison is required.
Processing evidence. Presence outcome, region location, image quality, and comparison signals where available.
Review record. Reference version, rule outcome, reviewer action, reason, and time — linked back to the source image.
Failure handling. Missing signatures, unavailable references, unusable images and processing failures each return their own outcome — never a quiet pass.
For extraction endpoints and the broader data contract, start with the bank check OCR API. Request a sample evaluation to establish the signature-specific fields and thresholds.
An example signature evidence record
Illustrative structure · not an API contract
{
"example_only": true,
"document": "cheque",
"image": { "width": 640, "height": 320 },
"signature": {
"presence": "candidate_present",
"box_px": {
"x": 365, "y": 174,
"width": 222, "height": 90
}
},
"comparison": {
"reference_set": "example-reference-v1",
"similarity": null,
"status": "not_evaluated"
},
"review": { "status": "pending" }
}The box matches the synthetic localisation diagram above. Similarity is left empty because no model has evaluated the drawing. Field names and response formats depend on the agreed integration.
Evaluation · Measure each step separately
A useful pilot includes the difficult documents.
Test genuine variation, missing signatures, poor scans, overlapping stamps, and known mismatches across your own layouts and document types. Agree the acceptance criteria before deciding what to automate.
| Stage | Measure | Question to answer |
|---|---|---|
| Detection | Precision and recall | Are signatures found, and are other marks incorrectly flagged? |
| Localisation | Box overlap and crop completeness | Does the region contain the signature without cutting off useful strokes? |
| Comparison | False acceptance and false rejection | At the chosen threshold, which mismatches pass and which genuine samples fail? |
| Operations | Unusable-image rate and manual-review rate | How much requires recapture or manual work, and can the team handle it? |
Report performance by document type, capture channel, layout, and image quality. Keep the evaluation set separate from calibration data, and record thresholds and reference versions with the results.
Signature detection & verification: your questions
What the plain version means, and what the system actually returns.
Does this work on documents other than cheques?
Does this work on documents other than cheques?
Does this tell us whether a signature is genuine?
Does this tell us whether a signature is genuine?
Can we use phone photos, or do we need a scanner?
Can we use phone photos, or do we need a scanner?
What happens when a signature is missing or the image is poor?
What happens when a signature is missing or the image is poor?
What is the difference between signature detection, localisation, and verification?
What is the difference between signature detection, localisation, and verification?
Can I integrate signature detection and comparison through an API?
Can I integrate signature detection and comparison through an API?
How does signature localisation work on a scanned document?
How does signature localisation work on a scanned document?
Can we keep document images and reference signatures on-premise?
Can we keep document images and reference signatures on-premise?
How many reference signatures should we hold per signer?
How many reference signatures should we hold per signer?
Can a scanned signature reveal pen pressure or writing speed?
Can a scanned signature reveal pen pressure or writing speed?
How are two handwritten signatures compared?
How are two handwritten signatures compared?
Can signature detection distinguish a blank field from a stamp or handwritten text?
Can signature detection distinguish a blank field from a stamp or handwritten text?
What accuracy should we expect from automated signature verification?
What accuracy should we expect from automated signature verification?
From illustration to evaluation
See what your document images can support.
Bring representative layouts — cheques, forms or both — together with your reference-signature process and the review outcomes you need. Define the detection, localisation, and comparison scope with the Chequedb team.
Discuss sample data, API scope, and deployment.