board game shelf parser
Use a board game shelf parser to turn shelf photos into import-ready game candidates with multi-stage detection, matching, and dedupe.
Game shelves are hard: narrow spines, partial occlusion, rotated text, and visually similar boxes. OCR alone misses too much on dense shelves.
A practical board game shelf parser combines object detection and text reading so it can detect boxes first and then improve title certainty.
What2Play runs multiple detection passes in parallel: Gemini upright detection, Claude shelf detection, and a Gemini spine-focused pass for narrow vertical titles.
After that, OCR text blocks are used as supplemental signals, then merged and deduplicated before catalog matching.
As a board game shelf scan app, parsing is most useful when it feeds recommendations and voting immediately. Imported titles should flow straight into candidate generation for new events.
This turns a shelf photo into a board game list you can actually use in planning, rather than a standalone scan result.
No. What2Play combines Gemini and Claude detection passes, a spine-focused scan, and OCR supplemental matching before dedupe and catalog lookup.
Yes. A dedicated spine-focused pass targets narrow vertical titles that are often missed by standard upright scans.
BGG sync and shelf parsing work best together. BGG covers linked collections, while shelf parsing helps with newly added or unsynced titles.
Yes. The parser converts shelf photos into import-ready candidates, then matches them to your local and BGG-backed catalog for a usable game list.
Yes. Hosts can review parsed candidates, adjust final game lists, and then run recommendations and voting with those updates.
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