Object detection & counting
Locate and count products, equipment, and other defined objects in images, with thresholds tuned to your operating conditions.
Computer Vision & OCR.
From pixels to progress.
Turn images, video, and documents into information your team can use. AsonTech Solutions builds visual AI around your capture conditions, business systems, and quality requirements.
Business-firstuse casesData-awarearchitectureMeasurabledelivery
Visual intelligence for real-world conditions.
Combine model development, document processing, and software integration in one delivery plan.
OCR & document extraction
Convert scans and photographs into searchable text and structured fields, with validation rules and review for uncertain results.
Visual quality inspection
Detect visible defects, missing components, and assembly variations against agreed inspection criteria.
Image classification & segmentation
Organize visual content and identify regions of interest to support measurement, search, and downstream workflows.
Video analytics
Analyze defined events and object movement in video, with access controls and retention appropriate to your use case.
Edge & mobile deployment
Bring visual inference to devices or cloud services based on latency, connectivity, hardware, and privacy requirements.
Less manual processing.
More useful information.
Practical applications for teams serving customers, managing information, and building digital products.
Manufacturing & quality
Flag visible defects and missing parts for inspection teams, with traceable image evidence.
Document operations
Extract invoice, form, and record fields into structured data with exception review.
Logistics & inventory
Count defined items, read labels, and support visual checks at operational checkpoints.
Field service & mobile
Assess installation images and guide recapture when photos do not meet quality requirements.
A clear path from idea to implementation.
Shared milestones keep business stakeholders and engineering teams aligned throughout delivery.
Scope the workflow
Agree on the objects, documents, decisions, and performance measures that matter.
Review the data
Assess image quality, variation, permissions, annotation needs, and coverage.
Prepare & label
Create consistent annotations and separate training, validation, and test datasets.
Develop the model
Build a baseline and refine the model and processing pipeline against task-specific measures.
Validate in context
Test lighting, blur, occlusion, layouts, device constraints, and failure handling.
Deploy & monitor
Integrate results into your workflow and monitor changing data and operational performance.
Build for the conditions that matter.
A model needs to work with the images your team actually captures. We evaluate changing lighting, document layouts, image quality, and device constraints, with review paths for results that need a second look.
Data access controlsImage quality checksHuman reviewHeld-out evaluationPerformance monitoringDocumented handover
Computer Vision & OCR FAQs
Answers about data, accuracy, document extraction, and deployment.
What does computer vision development include?
It includes data assessment, annotation, model development, evaluation, application integration, and deployment. The scope can cover images, video, scanned documents, or mobile capture.
How does OCR differ from document extraction?
OCR recognizes text in an image. Document extraction organizes that text into fields, tables, or records and applies checks before it enters a business system.
Can you work with our existing cameras or mobile app?
We assess resolution, capture conditions, interfaces, and hardware constraints, then plan an integration that fits your existing environment.
How much training data is required?
There is no universal number. Data needs depend on the task, variation, available pretrained models, and target performance. An initial data review identifies gaps and a practical collection plan.
How do you measure accuracy?
We select metrics for the task, such as precision and recall for detection or field-level accuracy for document extraction. Evaluation uses held-out examples and operating conditions representative of production.
Can the system work offline?
Some workloads can run on a mobile or edge device. Feasibility depends on model size, processing speed, memory, and acceptable accuracy. We validate those trade-offs before deployment.
What happens when the system is uncertain?
We can route low-confidence results to human review, request a clearer image, or stop an automated step. These behaviors are defined alongside your acceptance criteria.
Have a workflow in mind?
Tell us what you need to detect, read, or inspect. We’ll help define a practical first step.
Talk to Our Team