Project 01
The Unreadable Waybill
A lightweight workflow for converting difficult waybill images into barcode-linked shipment information.
- INTERNAL WORKFLOW
- CONFIDENTIAL DATA
- Category
- Computer Vision / Logistics Automation
- Role
- Computer Vision Intern
- Maturity
- Internal operational workflow
- Year
- 2026
30-second proof
What this proves
- Problem
- Damaged, torn, and low-quality waybill images could make barcode-linked shipment lookup slower or manual.
- Operational or business context
- Internal logistics workflow using confidential image samples.
- My contribution
- I designed and implemented image preprocessing, Code 128 decoding, and data-retrieval logic. I also integrated the decoder into an internal Slack-based workflow.
- Constraints
- Damaged and difficult images · Confidential validation data · Low-compute execution · Processing speed · Unresolved edge cases
- Project maturity
- Internal operational workflow
- Contextual technical stack
- Python · OpenCV · Code 128 decoding · Internal Slack workflow
Measurement noteResults are aggregate measurements from the reported confidential validation setup. The 98%+ figure refers to correctness among decoded outputs, not overall end-to-end resolution. Hardware details are not publicly stated.
Ownership
Designed
- I designed the preprocessing and decoding approach.
Implemented
- I implemented Code 128 decoding and data-retrieval logic.
Contributed to
- I contributed to internal workflow integration and validation.
Existing environment
- The work operated within an existing confidential logistics environment.
Not shown publicly
- Real waybills, internal Slack screens, and internal architecture are not shown publicly.
Overview
A lightweight workflow for converting difficult waybill images into barcode-linked shipment information.
The problem
Damaged, torn, or low-quality waybill images can make shipment lookup slower and more manual.
Context
The work belongs to confidential logistics operations, so public material must stay generalized and use abstract visuals.
My role
I worked on this project as a Computer Vision Intern.
What I built
I built a workflow that recovers barcode-linked shipment information from difficult waybill imagery.
How it works
The public sequence is intentionally high-level and avoids internal implementation details.
- Image received
- Preprocessing
- Barcode decoding
- Shipment lookup
- Result returned
Decisions
- I chose a lightweight decoding workflow to support fast, low-compute execution.
- I used preprocessing to improve difficult-image handling.
- I preserved unresolved cases rather than presenting uncertain outputs as correct.
Evidence and results
Validation used a confidential 1,000-image set. The 86.4% figure means end-to-end resolution across that validation setup.
The 98%+ figure refers only to correctness among successfully decoded outputs, not overall end-to-end resolution.
Some difficult inputs remained unresolved, and hardware details are not publicly stated.
Results are aggregate measurements from the reported confidential validation setup. The 98%+ figure refers to correctness among decoded outputs, not overall end-to-end resolution. Hardware details are not publicly stated.
Challenges and limitations
Some difficult samples may remain unresolved, and confidential documents or internal systems cannot be shown publicly.
Real confidential waybills, internal Slack interfaces, and internal architecture are not displayed. This project is not presented as open source.
What I would improve next
I would add approved sanitized diagrams, clearer failure-case categories, and a more explicit validation breakdown.