Computer Vision
Hire developers who build computer vision systems for inspection, counting, OCR and document scanning using OpenCV and YOLO.
Computer vision lets software read images and video: finding objects, reading text, checking products for defects or verifying an ID card. Developers typically combine OpenCV for image processing, detection models such as Ultralytics YOLO, and OCR engines, then run them on a server, in the cloud or on edge devices near the camera.
Results depend heavily on your own images, lighting and camera placement, so a good developer starts with a small data sample and an honest accuracy test. You can hire a computer vision developer per project, by the hour from $10/hr, or monthly for model retraining and support.
What We Build With It
- Build image processing pipelines with OpenCV: cropping, alignment, noise removal and measurement
- Train and fine-tune object detection and segmentation models such as Ultralytics YOLO on your labelled images
- Extract text from documents, invoices, number plates and ID cards with OCR and field validation
- Build image classification models for pass/fail quality checks on production lines
- Deploy models to cloud GPUs, on-premise servers or edge devices, and optimise them for speed
- Set up labelling workflows, accuracy reports and retraining as conditions change
Where It Fits Best
- Defect detection and quality checks in manufacturing
- KYC document and ID scanning for fintech and onboarding
- People, vehicle and stock counting from CCTV or warehouse cameras
- Invoice, receipt and form digitisation
- Safety monitoring such as helmet or restricted-zone detection on sites
Let's build somethingsimple & powerful
Frequently Asked Questions
How many images do we need to train a model?
It depends on how varied your objects and conditions are. A developer can often start with a few hundred labelled images for a pilot, test accuracy honestly, then collect more for the cases the model gets wrong.
Can it run on our existing CCTV cameras?
Often yes, if the resolution, angle and lighting show the objects clearly. A developer should review sample footage first, as some projects need a better-placed camera rather than a better model.
How accurate is OCR on Indian ID cards and documents?
Clean scans usually read well, but blur, glare, handwriting and regional scripts reduce accuracy. A sound system validates fields, flags low-confidence results for a person to check and never silently accepts a bad read.
Do we need a GPU?
Training usually benefits from a GPU, which can be rented in the cloud. Many trained models run fast enough on a CPU or a small edge device, depending on image size and how many frames per second you need.
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