Signs you need this
- Teams re-key data from scanned forms, photos or handwritten sheets.
- Inspection or verification depends on someone looking at every image.
- Document layouts vary too much for template-based OCR.
Overview
Paper forms, handwritten answer sheets, repair estimates and inspection photos hold information your systems can't use until it's extracted reliably. Computer vision makes that extraction dependable at scale.
We build OCR and image-understanding pipelines combining classical CV, modern detection models and multimodal LLMs, with confidence scoring and review queues so humans only see the hard cases.
How this differs from Deep Learning & Robotic Solutions: Computer Vision applies detection, OCR and multimodal models to documents and images; Deep Learning & Robotics covers custom neural architectures and perception for control systems.
What we deliver
- Document OCR pipelinesLayout analysis, handwriting recognition and field extraction from scanned forms.
- Image classification & detectionCustom-trained models for categorisation, defect detection and counting.
- Multimodal LLM extractionVision-capable models for semi-structured documents where templates vary.
- Quality scoringConfidence thresholds and human-review routing built into every pipeline.
- Video analyticsEvent detection and tracking from camera streams.
- Evaluation datasetsLabelled benchmarks so accuracy can be measured, not assumed.
Where it fits
Claims processingExtract fields from claim forms and repair estimates to feed fraud and settlement models.
Exam evaluationOCR handwritten answer sheets and pass them to rubric-based AI grading.
Proof of deliveryPhoto and signature capture validated at the point of delivery.
Retail and inspectionShelf, asset and defect analysis from images captured in the field.
Technology we use
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
OpenCVYOLOTesseractGoogle VisionGemini VisionPyTorchSegmentation modelsFastAPI
How we work
Sample audit
Review real documents or images to understand variance and edge cases.
Pipeline prototype
Measure extraction accuracy on a labelled sample before committing to a design.
Build & integrate
Production pipeline with review queues and integration into downstream systems.
Monitor & improve
Track accuracy over time and retrain as new document types appear.
Typical first engagement
An accuracy study on a labelled sample of your real documents or images, with a go/no-go recommendation and pipeline design.
Common questions
How accurate is handwriting OCR?
It depends on the source; combining OCR engines with multimodal models and a confidence-based review queue keeps the effective accuracy high.
Can this run without sending images to a cloud API?
Yes. Open models such as YOLO and Tesseract can run on-premise when privacy requires it.
What about photos taken on phones in the field?
We design for it: rotation, glare and blur handling, plus capture guidance in the app itself.
