Opera Rolandi archive

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 and Finalize Report and Wikipage (Step 4: Generalization)
17.12. (week 14)	Final Presentation

Step 1

• Train model on diverse random images of Rolandi’s libretti collection (better for generalization aspects)
• Test model on diverse random images of Rolandi’s libretti collection
• Test on a single chosen libretto:
  • If bad results, train the model on more images coming from the libretto
  • If still bad results (and ok with the planning), try to help the model by pre-processing the images beforehand : i.e. black and white images (filter that accentuates shades of black)
• Choose a well-formatted and not too damaged libretto (to be able to do step 2)

Step 2

• Extract essential information from the libretto with OCR
  • names, scenes and descriptions
  • if bad results, apply pre-processing to make the handwriting sharper for reading

• If OCR extracts variants of same name:
  • perform clustering to give it a common name
  • apply distance measuring techniques
  • find the real name (not just the abbreviation) of the character using the introduction at the start of the scene

Step 3

For one libretto:

• Extract the information below and store it in a tree format (json file):
• Assessment of extraction and cleaning results
• Create the relationship network:
  • nodes = characters’ name
  • links = interactions
  • weight of the links = importance of a relationship
  • weight of nodes = speech weight of a character + normalization so that all the scenes have the same weight/importance

Step 4: Optionnal

• See how our network algorithm generalizes (according to the success of step 1) to five Rolandi’s libretti.
• If this does not generalize well, strengthen our deep learning model with more images coming from these libretti.

Description of the Methods

Dataset

• Choice of datasets (Format du texte, lisibilité des noms, séparation des scène/names propre)
• Choix de tout créer en se basant sur Antigone

Segmentation

• Choix de segmenter que les scènes et les abréviations des personnages
• Choix de ne pas se focaliser sur les dialogues (car impact de l'orientation du scan qui perturbe l'extraction des coordonnées des box et ainsi la remise en ordre des box dans une suite logique)
• Exemple de segmentations effectuées (background vert, noms rouge, ...) + nombre d'exemples pour training et validations
• Accuracy du testing ?
• Using the dh_segment model, we return for each pixel of the testing images a probability to be in the green, red, yellow or blue classes. This list of class probabilities has the same length as the image from which the words are going to be extracted.

OCR

• Use Google Vision API to extract all the words of our scans
• We store each word and its box coordinated into a csv file
• Using the prediction classes probabilities for each pixel of the image, which were computed using dh_segment, we can now create a mask to extract the words belonging to these classes:
  • As the probability is distributed among all classes, one has to define an ocr_threshold which specifies at which point one pixel of the image belongs to one class. This 1 and 0 values will be stored in a mask for each attribute.
  • We define a range of the image from which to extract the words for each attribute (i.e. for the extraction of the names, we want to extract in the most left part of the left page, and the most left part of the right page.)
  • When we focus on a word and its particular box, we compute the mean of the class probability of all the pixels figuring in a smaller ratio of the box, which is centered in the true box (i.e. we don't read all the pixels of the box as the pixels further away from the center of the box have less chance of being in the class.)
  • We define an mean_threshold for each attribute which specifies at which point the word in the box is kept as being part of the class.
• We return for each extracted word its top left box x and y coordinate (a box is created to encapsulate the word in the text and has x and y coordinates for each corner).
• We first order the words based on their x coordinates, meaning if they belong to the left or right page. This is done using the defined ranges of the image from which to extract the words for each attribute. Then we order the words based on the y coordinates from top to bottom.
• In the end, we have for each page the extracted words and their respective attribute (name, scene, description) in the order of appearance.

Combination of OCR and Segmentation to Find Words of Interest

For Names:

• Extract essential information from the libretto with OCR (names, scenes and descriptions)
• Clean variants of same name by applying distance measuring technique of Levenstein
• Explain why clustering wasn't working: k-means works if we already know the number of clusters, which is not our case. K-metroids can be a possible solution but complicated to implement for strings. Because of time constraints, we decided to focus on common similarity distances, and in our case an edit distance being the Levenstein.
• Link real name to the abbreviation by using the description field (pattern matching)

For Scenes:

• Extract occurence of Scene and its variance
• Find mention of "Scena PRIMA" to delimit where the Acts start
• Increment each Scene mention by 1 to give its value
• Problem encountered: As the word SCENA PRIMA takes a lot of space, this word is counted as being two scenes. Therefore we need to remove "SCENA" in the "SCENA PRIMA" to not count it twice and increment wrongly.

Return a dictionnary containg in a tree strcture format the Acts values, Scenes values and names of characters appearing in the scene attached with their number of occurences in the scene.