Vision & OCR
Eyes for documents, IDs and the field.
Most vision models fall apart on African documents and field conditions. We fine-tune and distil models specifically for local IDs, rural imagery and low-bandwidth capture — and ship them with the pre-processing pipelines that actually matter.
99%+
Field accuracy on EAC ID cards
50 ms
On-device inference (ONNX)
20M+
Documents in training archive
5×
Faster than manual keying
What we ship
Capabilities
Computer vision and OCR for African ID cards, handwritten forms, plates, agriculture imagery and scanned legacy archives — robust to low-light and low-bandwidth.
- 01
ID, passport & driver licence OCR
East African ID card templates — Tanzania NIDA, Kenya Huduma Namba, Uganda NIN — extracted with 99%+ field accuracy on real-world captures.
- 02
Handwritten Swahili form extraction
Land titles, clinic ledgers and tax forms digitized with layout-aware OCR trained on decades of government archives.
- 03
Plate recognition & toll automation
East African plate formats — including partial, dirty and angled plates — recognized at speed with on-device ONNX models.
- 04
Agri & remote-sensing image models
Crop health, flood mapping and land-use classification trained on local satellite and drone imagery for the East African climate zone.
Outcomes
- Automate document-heavy back-office work
- Digitize decades of paper archives
- Real-time recognition in the field and on the road
Tech we use
In the field
- 1
KYC for digital lenders
ID extraction and liveness check in under 4 seconds — fully on-device, no PII leaves the phone.
- 2
Land registry digitization
30 years of handwritten Tanzanian land titles digitized and indexed in 6 months.
- 3
Toll-plaza automation
Plate recognition at 140 km/h on East African regional highways — reducing lane dwell time by 80%.
How we deliver
Our delivery process
Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.
- Step01
Document taxonomy
We catalogue every form type, ID layout and handwriting style in scope — the model is only as good as its training distribution.
- Step02
Synthetic augmentation
Low-light, motion blur, ink fade and scan artifacts are synthesized to make the model robust to field conditions.
- Step03
Post-OCR validation
Regex, checksum and cross-field validators catch extraction errors before they reach your database.
- Step04
Edge deployment
ONNX-quantized models for on-device inference — no cloud dependency for field agents with intermittent connectivity.
Ready to get started?
Build vision & ocr for your product
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