3.8 / 5

Shelf Scanner

Scan your bookshelf, get a digital catalog with reading recommendations

Fun

Instant

The Problem

No easy way to catalog physical books; can't remember what you own; buying duplicates

Current Solutions (Not Great)

Manual entry (tedious), barcode scanning apps (one by one), Goodreads (manual)

Who Needs This

Book lovers, collectors, people moving houses, libraries, anyone with 50+ books

You have books everywhere but no good record of what you own. Shelf Scanner photographs your bookshelf and identifies every book from its spine: title, author, cover, genre. In one sweep, you get a digital catalog of your library. But it goes further: based on what you own, it recommends what to read next, identifies gaps in your collection, spots duplicates, and suggests books that friends might want to borrow. Your personal library, digitized and analyzed, without manually entering each book.

Honest Take

This is a fun novelty for book collectors, but it's a pretty narrow use case—most people don't catalog their home libraries. Goodreads has strong network effects, and honestly, manual entry isn't painful enough for most users to seek alternatives. Could work as a free viral tool with affiliate monetization, but don't expect huge adoption.

Monetization Ideas

Ways to turn this into revenue

Freemium

Free basic, $5-15/mo for pro

Affiliate / Referral

10-30% commission on referrals

One-Time Purchase

$9-49 per license

Features

Key features that make this app valuable

Multi-book spine recognition
Title and author extraction
Book metadata enrichment
Digital library catalog
Reading recommendations
Collection analysis (genres, authors)
Duplicate detection
Want-to-read tracking
Lending tracker
Export and sharing

Build Prompt

Use this prompt with an AI assistant to start building

Build a React PWA called 'Shelf Scanner' using WebInfer with vision capabilities. UI: camera for shelf scanning, library grid view, book detail pages, recommendations tab. Capture: photograph bookshelf (works best with spines visible). Vision model analysis with generateObject returns { books: [{ spineText: string, estimatedTitle: string, estimatedAuthor: string, confidence: number, spinePosition: {x,y,w,h} }] }. Metadata enrichment: for each detected book, search local book database (or use AI knowledge) to get: full title, author, cover image, genre, publication year, description, page count. Display: grid of book covers with titles. Book detail: full metadata, notes field, read/unread status, rating, lent-to tracker. Collection stats: books by genre pie chart, favorite authors, decades published. Recommendations: based on owned books, generate reading suggestions. Use generateObject: { recommendations: [{ title: string, author: string, reason: string, similarTo: string }], genreGaps: string[], authorDeepDives: string[] }. Want-to-read list: add recommended books, shows which you already own. Duplicate detector: flags potential duplicate purchases. Export: CSV, Goodreads import format. Share: 'My library' shareable link with selected books. Affiliate links for recommended book purchases.

Score Breakdown

Pain

Clarity