logistics_ops · logistics · workflow
Nanonets automates shipping container recognition for Adani Ports with Computer Vision and OCR
APSEZ's container inspection was entirely manual, causing misread container numbers due to distance, angle, lighting, and inspector oversight, resulting in delays of up to 10 minutes per container and additional expenses.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Motion detection at gate
Motion detection monitors the entry of containers at port gates.
Tools used
NanonetsOCRDocker
Outcome
Nanonets deployed an on-premise computer vision and OCR system that fully automated container data capture at port gates, reducing data transfer by over 10,000x and building a self-improving model that increases accuracy over time.
Results
Time saveddelays of upto 10 minutes per container
Volumeover 10,000x
Grounding & classification
Source type: vendor customer story
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computer visionfraud detectionocrquality inspectionmetric backednamed customerproduction runtime claimedtools describedworkflow describedlogisticsautomation ratecycle time reductionerror reductionvendor customer storycompliance monitoringlogistics opsextract classify routemonitor detect alert