When people hear “computer vision,” they think of self-driving cars, facial recognition, and autonomous robots. These are the headline applications, and they dominate the conversation. But the most impactful applications of computer vision in business are far less dramatic and far more immediately useful.
Document processing. Quality inspection. Inventory management. Content extraction from images and video. These are the tasks where computer vision delivers measurable ROI today, without requiring a fleet of GPUs or a team of ML researchers. The combination of foundation models with vision capabilities and mature open-source tools has made computer vision accessible to engineering teams that would not have touched it five years ago.
At CONFLICT, we have integrated computer vision into client systems across multiple domains. PlanOpticon uses it to extract diagrams and visual content from meeting recordings. Client projects have applied it to everything from automated document classification to retail shelf analysis. This article covers what is practical today, what requires custom work, and how to make the build-vs-buy decision.
Document Processing: The Highest-ROI Application
Every organization processes documents. Invoices, contracts, forms, receipts, IDs, insurance claims, compliance filings. Most of this processing still involves humans reading documents and typing information into systems. Computer vision can automate a significant portion of this work.
The technology stack for document processing has matured considerably:
OCR (Optical Character Recognition) extracts text from images. Modern OCR engines handle printed text with near-perfect accuracy. Handwriting is harder but achievable for structured forms where the handwriting appears in defined regions.
Layout analysis identifies the structure of a document: headers, paragraphs, tables, signatures, stamps. This is critical for extracting meaning, not just text. Knowing that “42,500” is a text string is less useful than knowing it appears in the “Total Amount” column of a table.
Document classification determines what type of document you are looking at. Is this an invoice, a contract, a receipt, or a form? Classification routes the document to the appropriate extraction pipeline.
Information extraction pulls specific fields from classified documents. For an invoice: vendor name, date, line items, total amount, payment terms. For a contract: parties, dates, obligations, termination clauses.
The modern approach combines vision-capable LLMs with traditional OCR for a pipeline that handles diverse document types without custom training per document class:
def process_document(image_path: str) -> dict:
# Step 1: OCR for text extraction
raw_text = ocr_engine.extract(image_path)
# Step 2: Vision LLM for layout understanding and field extraction
result = vision_llm.analyze(
image=image_path,
prompt="""Analyze this document. Return structured JSON with:
- document_type: classification of the document
- fields: key-value pairs of extracted information
- tables: any tabular data as structured arrays
- confidence: your confidence in each extraction""",
response_format="json"
)
# Step 3: Validate extracted fields against OCR text
validated = cross_validate(result, raw_text)
return validated
The cross-validation step is important. Vision LLMs sometimes hallucinate field values – confidently extracting a number that does not actually appear in the document. Comparing extracted values against the OCR text catches these errors.
For organizations processing hundreds or thousands of documents daily, even a partial automation of this pipeline produces significant cost savings. The typical pattern is to automate high-confidence extractions (70-80% of documents) and route the rest to human review. This does not eliminate the document processing team – it lets them focus on the documents that actually need human judgment.
Quality Inspection: Where Vision Meets Manufacturing
Visual quality inspection is one of the oldest applications of computer vision, and it remains one of the most valuable. Manufacturing lines, food processing, packaging, textile production – any process where defects are visually identifiable is a candidate.
The landscape has shifted in two important ways:
Pre-trained models handle common defects. Scratches, dents, color variations, missing components, alignment errors – these defect types are general enough that transfer learning from pre-trained vision models works well. You do not need to train a model from scratch. Fine-tuning a pre-trained model on a few hundred examples of your specific defects often produces production-quality results.
Edge deployment is practical. Modern vision models can run on edge devices (NVIDIA Jetson, Intel NCS, even capable ARM processors) at speeds fast enough for production line inspection. This means the vision system can inspect items as they move through the line without adding latency.
The engineering challenge in quality inspection is less about the model and more about the imaging setup:
Lighting consistency. Variations in lighting cause more false positives and negatives than model limitations. Controlled, consistent lighting is the single most impactful investment in a vision inspection system.
Camera positioning and resolution. The camera needs to capture the features relevant to quality at sufficient resolution. A 4K camera positioned too far away is worse than a 1080p camera positioned correctly.
Throughput requirements. A production line moving at 200 items per minute gives you 300 milliseconds per inspection. Your entire pipeline – image capture, preprocessing, inference, decision – needs to fit in that window.
Edge case handling. What happens when the system is uncertain? Reject the item for manual inspection? Pass it through? The cost of false positives (rejecting good items) versus false negatives (passing bad items) determines your threshold, and this is a business decision, not a technical one.
We have found that pilot projects in quality inspection are quick to deliver value because the success criteria are clear and measurable: detection rate, false positive rate, throughput. If your inspection process currently relies on human visual inspection, even a system that handles the obvious cases (70-80% of items) reduces human workload and improves consistency.
Inventory Management and Retail Analytics
Computer vision for inventory management and retail environments has moved from experimental to practical. The applications break down into several categories:
Shelf monitoring. Cameras mounted in retail environments can detect out-of-stock items, misplaced products, incorrect pricing labels, and planogram compliance. This replaces or supplements manual store walks. The technical challenge is that retail environments are visually complex – products are densely packed, lighting varies, and shoppers occlude the shelves.
Warehouse inventory. Drones or fixed cameras in warehouses can identify pallet locations, read labels, and track inventory movement. For large distribution centers, this provides real-time inventory visibility that manual counting cannot match.
Damage detection. Identifying damaged packaging in transit or storage before it reaches customers. This is a variant of quality inspection applied to logistics.
The practical approach for most organizations is to start with a constrained problem – a single shelf section, a single warehouse aisle – and demonstrate value before scaling. Computer vision systems in complex environments require iteration: tuning detection thresholds, handling edge cases, adapting to seasonal changes in product mix.
Content Extraction From Images and Video
This is the application space where we have the most direct experience through PlanOpticon. Extracting meaningful content from visual media is a broad category that includes:
Diagram and whiteboard extraction. Converting photos of whiteboards, hand-drawn diagrams, and presentation slides into structured data. Vision LLMs have made this dramatically easier – a model like GPT-4o can describe the contents of a diagram, identify relationships between elements, and output structured representations.
Video content analysis. Identifying key frames, classifying visual content, extracting text overlays, and detecting scene changes. PlanOpticon does this for meeting recordings, but the same techniques apply to training videos, security footage, broadcast content, and user-generated video.
Chart and graph interpretation. Extracting data from charts and graphs embedded in documents or presentations. This is surprisingly useful – a significant amount of business data lives in PowerPoint charts and PDF reports rather than in databases.
The pipeline pattern for visual content extraction:
Frame selection. For video, identify the frames worth analyzing. Not every frame is informative. Scene change detection and content scoring can reduce thousands of frames to dozens of key frames.
Classification. Determine what type of visual content you are looking at. Is it a slide? A whiteboard? A person speaking? A diagram? Classification determines which extraction pipeline to apply.
Extraction. Apply the appropriate extraction logic. For text-heavy content, OCR plus layout analysis. For diagrams, vision LLM analysis. For data visualizations, chart interpretation.
Structuring. Convert extracted content into structured formats: JSON, Markdown, knowledge graph nodes. Raw extraction is useful for search; structured extraction is useful for integration with other systems.
In PlanOpticon, this pipeline processes meeting recordings and produces structured knowledge: topics discussed, decisions made, action items assigned, diagrams captured, and relationships between all of these. The computer vision components – frame classification and diagram analysis – are critical to capturing the visual content that audio transcription misses.
What Requires Custom Training
Not everything can be handled by pre-trained models and vision LLMs. Some applications genuinely require custom model training:
Highly specialized visual domains. Medical imaging (radiology, pathology, dermatology), satellite imagery analysis, microscopy, and geological survey images have visual characteristics that differ substantially from the natural images that foundation models were trained on. Fine-tuning or custom training is usually necessary.
Real-time detection with strict latency requirements. If you need to detect specific objects at 60fps on an edge device, you likely need a custom-trained detection model (YOLO, EfficientDet) optimized for your specific object classes. Vision LLMs are too slow for this use case.
Sub-millimeter precision. Manufacturing tolerance checking at very fine resolution requires custom-trained models with carefully calibrated imaging systems. General-purpose models do not have the precision.
Domain-specific anomaly detection. Detecting anomalies in contexts where “normal” is highly specific to your domain (specific machine types, specific biological specimens, specific material textures) usually requires training on your data.
The build-vs-buy decision comes down to: can a general-purpose model or API achieve acceptable accuracy on your task? Test this first. Run your actual data through available vision APIs and measure the results. If accuracy is above your threshold, you do not need custom training. If it is not, you need to evaluate whether the gap can be closed with prompt engineering, few-shot examples, and post-processing – or whether custom training is genuinely required.
Custom training involves data collection (which means labeling, which is expensive and time-consuming), model selection, training infrastructure, evaluation, and ongoing maintenance. The total cost is significant, and it only makes sense when the task cannot be addressed adequately with available tools.
Getting Started: A Practical Framework
If you are evaluating computer vision for your organization, here is a framework for getting started:
Identify the visual task. What are humans looking at, and what decisions are they making based on what they see? Document processing, quality inspection, and content extraction are the highest-ROI starting points.
Quantify the opportunity. How many images or documents are processed per day? What is the current cost per item? What error rate is acceptable? These numbers determine whether automation is worthwhile and what accuracy threshold you need to hit.
Prototype with available tools. Use vision APIs (OpenAI, Google Cloud Vision, AWS Rekognition) or open-source models to test your actual data. Measure accuracy, latency, and cost. This prototype costs days, not months.
Evaluate the gap. If the prototype meets your accuracy requirements, build the production pipeline around it. If not, evaluate whether the gap is addressable through engineering (better preprocessing, hybrid approaches, ensemble methods) or requires custom training.
Design for human fallback. Your first deployment should route uncertain cases to human reviewers. This gives you production data on actual accuracy and builds confidence in the system before you increase automation.
Iterate on the data. The most common way to improve a computer vision system is to improve the input: better image quality, more consistent lighting, cleaner documents. Before investing in model improvements, invest in data quality.
Computer vision has reached a practical inflection point. The combination of capable vision LLMs, mature open-source tools, and accessible cloud APIs means that engineering teams can build useful vision applications without specialized ML expertise. The opportunities are not in self-driving cars. They are in the documents, images, and visual processes that your organization already handles every day.
