Opera Rolandi archive: Difference between revisions
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| | |12.11. (week 9) | ||
| | |Step 1: Segmentation model training, fine tuning & testing | ||
| | |- | ||
|19.11. (week 10) | |||
|Step 2: Information extraction & cleaning | |||
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|26.11. (week 11) | |||
|Finishing Step 2 | |||
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|12 | |03.12. (week 12) | ||
| | |Step 3: Information storing & network visualization | ||
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| | |10.12. (week 13) | ||
| | |Finishing Step 3 (Step 4: Generalization) | ||
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| | |17.12. (week 14) | ||
| | |Finalize Report and Wikipage | ||
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Revision as of 09:32, 14 November 2020
Abstract
The Fondazione Giorgio Cini has digitized 36000 pages from Ulderico Rolandi's opera libretti collection. This collection contains contemporary works of 17th- and 18th-century composers. These opera libretti have a diverse content, which offered us a large amount of possibilities for analysis.
This project chose to concentrate on a way to illustrate the characters’ interactions in the Rolandi's libretti collection through network visualization. We also highlighted the importance of each character in the libretto they figure in. To achieve this, we retrieved important information using Deep Learning models and OCR. We started from a subset of Rolandi’s libretti collection and generalized this algorithm for all Rolandi’s libretti collection.
Planning
| Week | To do | |
|---|---|---|
| 12.11. (week 9) | Step 1: Segmentation model training, fine tuning & testing | |
| 19.11. (week 10) | Step 2: Information extraction & cleaning | |
| 26.11. (week 11) | Finishing Step 2 | |
| 03.12. (week 12) | Step 3: Information storing & network visualization | |
| 10.12. (week 13) | Finishing Step 3 (Step 4: Generalization) | |
| 17.12. (week 14) | Finalize Report and Wikipage |