Visual Inventory Tracker

The Problem

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Consolidating information about products you own such as warranty status, retailer, return-window status, and even where it's located in your house!
This is where Shelf comes in!

The Idea

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Have you ever lost track of your receipts? Forgotten when to return your product?
Lost your warranty? Do you even know where your item is in your home?

Stop worrying, Shelf Itâ„¢!

Shelf consolidates information about the products you own, such as warranty status, return window, purchase date and where it's located in your home! Using image-to-text recognition, we extract information directly from your receipts and store that data into our centralized database. You can set reminders for returns and warranties, so that you can be notified before they expire!

Development Timeline

Basic Mockups

These are our initial, proof-of-concept mockups that will be iterated on.

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Mockup 1

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Mockup 2

Intermediate Mockups

After receiving initial feedback, we developed the following mockups:

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Mockup 1

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Mockup 2

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Mockup 3

Peer Review Feedback

During peer feedback sessions, we received input from students and members of the public regarding both our idea and our UI mockups.
Most people indicated that they prefer simple interfaces with minimal colour rather than a more colourful/playful interface.
Their responses to our survey questions validated a need for our product and showed that most, if not all people experience inventory tracking difficulties.

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UI Progress

We implemented the best-received UI mockup (Intermediate Mockup A), although going with a green theme instead of the original white/grey.
Currently our bottom tab bar is still under construction and some other UI components will need a few changes to maintain theme consistency.

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Login Screen

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Shelves Menu

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Items Menu

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Item Details

Backend Progress

We wanted to provide users with a new and interesting way to interact wth our app through Apple's Vision API.
We started with the text detection demo from WWDC17, implementing the ability to take a picture of some printed words and convert it into text.
From here, we will strengthen the recognition by accounting for device rotation and allowing users to directly select a section of text in an image.

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Challenges and Successes

As with any endeavour, we can expect to experience both challenges and successes.
Most of our challenges arose from significantly reduced development time due to trips.
Most of our successes revolve around backend breakthroughs.

Challenges

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Successes

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Developed by: Raza, Sharon, Mike, and Andrew