Computer-vision inventory system
A system combining computer vision and data processing to identify and classify items and support inventory analysis.
Personal learning and demonstration project. It is not a deployment for a real client.
Perception
- Image
- Object detection (YOLO)
- Text reading (OCR)
Data
- Embeddings for classification
- Data validation
- Inventory record
Conceptual diagram based on the project description. It will be replaced by my own diagram or screenshots.
Overview
A technical project combining computer vision and data processing to identify and classify items and support inventory analysis. It is presented as a prototype; it is not described as a finished product or a production deployment.
Problem and goal
Counting and recording items by hand takes time and creates transcription errors. The goal was to test how far items can be identified from images, the text on them extracted and the result turned into validated inventory data.
Technical architecture
The system combines several techniques, each with a different role:
- YOLO detection to locate objects in the image.
- OCR to read text accompanying the items (labels, references).
- Embeddings to compare and classify elements by similarity.
- Data validation and error handling so that a doubtful recognition does not enter the inventory unchecked.
I’m happy to walk through the implementation details (engines, models and specific parameters) in a conversation.
Technologies
Python, YOLO, OCR and embeddings.
Challenges and decisions
- Confidence and review. No detection is perfect. A useful system decides what to accept automatically, what to flag for review and what to reject.
- Clean data before bigger models. Many inventory errors come from poorly captured data, not from the model; validation matters as much as recognition.
- Narrow scope. A small set of well-solved categories beats unreliable general recognition.
Results and evaluation
Evaluation metrics are not published yet: no unvalidated figures are shown.
Relation to services
Systems like this connect with process automation and AI implementation when the data source is images or documents. I have also worked on freelance image-segmentation and playing-card recognition projects, described on the experience page.