Container number reading: Datasets & Models

In logistics and warehousing operations, manually recording container numbers at gates and yards is slow, error-prone, and hard to scale across shifts and locations. A single transposed digit can misroute a container or corrupt inventory records, and staff cannot keep pace during peak throughput. Automating container number reading with an OCR model captures identifiers in real time, cross-checks the ISO check digit for accuracy, and feeds clean data directly into terminal operating and warehouse management systems.

Automated container number reading reduces manual data entry, speeds up gate and yard processing, and improves the accuracy of inventory and tracking records. It runs on existing fixed and mobile cameras, so teams can deploy across multiple entry points without new hardware, and a container number dataset can be expanded and retrained to handle new angles, lighting, and weathered markings as operations scale.

1. Upload an image: Drop any photo into the widget above to try container number reading for free, right on this page.
2. Preview instant results: The model detects shipping container, container number, check digit, container door, license plate within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train OCR / document model on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.