Object Detection for Documents: From Signatures to Logos

OCR (Optical Character Recognition) has long been the standard for document automation, making text searchable and editable. But many business processes also depend on non-text elements (signatures, logos, stamps, or photos) that OCR alone can’t handle.

This is where object detection comes in. Powered by computer vision, it can locate and classify visual elements inside documents at scale. From detecting signatures on checks to recognizing logos on invoices, object detection extends automation beyond text.

In this article, we’ll explore how object detection works compared to OCR, key use cases (signature, logo, and photo detection) and the business benefits of adopting this technology.

What is Object Detection in Document Processing?

Object detection uses AI to identify and locate elements within an image, drawing bounding boxes around objects and classifying them.

In documents, this makes it possible to detect:

• A missing or valid signature.
• The presence of a company logo.
• A photo in a resume.
• A required stamp or seal.

These capabilities unlock new automation scenarios: banks can flag unsigned checks, accounting tools can classify invoices by logo, and HR platforms can extract applicant photos automatically.

In short, object detection complements OCR, focusing on visual patterns instead of text.

How Object Detection Works

At a technical level, object detection involves training a computer vision model on annotated data.

1. Input: A document.
2. Feature extraction: The model analyzes shapes, patterns, and regions of interest.
3. Bounding boxes: It highlights detected objects with coordinates.
4. Classification: Each object is labeled (e.g., "signature"; "logo", "photo").
5. Integration: Results flow into automation workflows, such as verifying signatures or routing invoices.

Unlike OCR, which focuses on characters, object detection works even when there’s no text to recognize.

Use Case 1: Signature Detection

One of the most common applications of object detection in documents is signature detection.

Why Signature Detection Matters

Example: Detecting Signatures on Bank Checks with Mindee

You can implement signature detection using Mindee’s platform in just a few steps:

1. Add a signature field (as photo detection) to your checks model using the AI assistant.
2. Upload the check (PDF or image) via the API or the dashboard.
3. Mindee’s object detection engine analyzes the document and detects the signature area.
4. The signature is extracted as an image, alongside other structured fields (amount, date, account number, etc.).
5. You receive a JSON response containing the location data of the signature image.

Here’s an example of how it looks on the Mindee platform:

Signature detection is also useful for:

• Contracts: confirming all required parties have signed.
• Consent forms: verifying authorization before moving forward.
• Government paperwork: validating identity confirmation fields.

Use Case 2: Logo Detection

Logos are a key element in many types of documents, especially for invoices, receipts, and brand-related paperwork. Detecting them automatically can save hours of manual work.

Why Logo Detection Matters

Example: Detecting Logos on Invoices with Mindee

1. Ask the AI agent to add a logo field (as photo detection) to your Invoice model.
2. Upload invoices or receipts to the Mindee platform.
3. The object detection engine searches for logos in the document.
4. Detected logos are highlighted and extracted, alongside other fields like invoice number, date, and total.
5. Results are delivered via JSON API response, making it easy to connect with ERP or accounting tools.

Here’s an example of how it looks on the Mindee platform:

Beyond Invoices

Logo detection is also useful for:

• Receipts: extracting retail logos for expense reports.
• Insurance claims: detecting manufacturer logos on product-related documents.
• Legal or compliance checks: identifying trademark use in archives.

Use Case 3: Photo Detection

Photos appear in resumes, ID documents, and applications where visual identification is required. Detecting them automatically reduces manual sorting and validation work.

Why Photo Detection Matters

Example: Detecting Photos in Resumes with Mindee

1. Ask the AI assistant to add a candidate picture field as photo detection from your Resume model,
2. Upload resumes or ID documents via the dashboard or API.
3. Mindee’s model analyzes the layout and detects photo zones.
4. JSON output includes metadata to confirm the presence or absence of a photo.

This can be integrated directly into ATS (Applicant Tracking Systems) or KYC (Know Your Customer) platforms.

Here’s an example of how it looks on the Mindee platform:

Beyond Resumes

Photo detection is also useful for:

• ID documents: verifying the presence of a passport or ID portrait.
• Applications: detecting photos on forms where they’re mandatory.
• Education/visa processes: ensuring candidate or applicant photos are attached.

While OCR remains essential for text extraction, object detection unlocks new automation opportunities. Businesses can now detect and extract visual elements — from signatures and logos to photos, stamps, and checkboxes.

• Signature detection helps banks, insurers, and legal teams fight fraud and ensure compliance.
• Logo detection accelerates accounting, expense management, and brand protection.
• Photo detection supports HR, identity verification, and regulatory needs.

By combining OCR with object detection, organizations can build smarter, more reliable, and fully automated document workflows.

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