User:YvanZo/Draft/Cover Art Metadata Recognition: Difference between revisions
(Initial draft of an idea proposed during the summit 23) |
(→Metadata recognition from cover art: Explain the difficulty and consider ML for a very long project) |
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The optical character recognition engine [https://github.com/tesseract-ocr/tesseract Tesseract] can be used through either Naptha’s port in JavaScript [https://tesseract.projectnaptha.com/ Tesseract.js] or Knight’s build in WebAssembly [https://robertknight.github.io/tesseract-wasm/ tesseract-wasm]. |
The optical character recognition engine [https://github.com/tesseract-ocr/tesseract Tesseract] can be used through either Naptha’s port in JavaScript [https://tesseract.projectnaptha.com/ Tesseract.js] or Knight’s build in WebAssembly [https://robertknight.github.io/tesseract-wasm/ tesseract-wasm]. |
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In either case, the web user interface has to be written in React.js to allow a future integration to the website. |
In either case, the web user interface has to be written in React.js to allow a future integration to the website. |
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Tesseract has a lot of parameters that allow tuning it for specific usage, or focusing on some selected areas. |
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However the main part of the project might be to turn its output into something useful. |
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The parsing/mapping can potentially be achieved through machine learning but that would likely double the project length. |
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Revision as of 18:19, 29 November 2023
Metadata recognition from cover art
Proposed mentors: bitmap, reosarevok, yvanzo
Languages/skills: React.js, WebAssembly
Forum for discussion
Estimated Project Length: TBD hours
Difficulty: TBD
MusicBrainz gathers metadata about releases and their cover art through the Cover Art Archive. Very often editors have to type the data contained in the cover art images. A drastic boost for them would be to programmatically parse these images to extract as much metadata as possible: free text, title, artist credit, label code, barcode, tracklist…
The optical character recognition engine Tesseract can be used through either Naptha’s port in JavaScript Tesseract.js or Knight’s build in WebAssembly tesseract-wasm. In either case, the web user interface has to be written in React.js to allow a future integration to the website.
Tesseract has a lot of parameters that allow tuning it for specific usage, or focusing on some selected areas. However the main part of the project might be to turn its output into something useful. The parsing/mapping can potentially be achieved through machine learning but that would likely double the project length.
