France: Exploring Historical Cookbooks

Introduction

In this project, our main goal is to

obtain the height information of buildings in Venice city. In order to achieve this goal, we construct a point cloud model of Venice from Google Earth and YouTube drone videos with the help of photogrammetry tools. Initially, we experimented on drone videos from YouTube. Since our goal is to detect the height of all the buildings in Venice, it is important for us to collect images of all the buildings in the city. However, in YouTube videos, we only found some landmark architecture whose height information is already available on the Internet. Hence, we switched the data scource to Google Earth images. With our current image source, we have successfully calculated the heights of Venice buildings including unknown buildings and famous ones. We also evaluated both subjective and objective quality of our City Elevation Map, which will be discussed in the following sections. The Venice 3d model along with height information can be used to create an virtual experience. Besides, the City Elevation map can be useful for urban planning and security purposes in the future.

Research questions

1. What were the main ingredients used in 1900 in France?

2. Can we observe a difference per region?

Project Plan and Milestones

Date	Tasks	Completion
Week 3	Find multiple French cookbood in French or English from different times. Prepare slides for the initial project idea presentation.	✓
Week 4	Compare different cookbooks, consider the OCR scan and think of possible research questions. Discuss with TAs the goal and implementation of the projects.	✓
Week 5	Decide on one French cookbook. Scan the physical book page by page. Sanitize the initial dataset.	✓
Week 6-7	Separate files by region Give OCR scan for the pages. Start to construct the dataset.	✓
Week 8-9	Prepare for midterm presentation. Construct dataset and think of data structure to store the information.	✓
Week 10	Set up the GitHub repository. Finish the creation of the dataset	✓
Week 11	Fix bugs in extraction script and take exceptional cases into consideration. Create categories for ingredients.	✓
Week 12	Perform the data processing of the ingredients. Exploratory analysis of the dataset.	✓
Week 13	Further improve the dataset. Overall analysis & Per Region analysis.	✓
Week 14	Prepare the final presentation Finish the Wikipedia page	✓

Methodology

Data collection

For a start, we scanned a physical French cookbook.

• 

  French cookbook

• 

  A page of the scanned book

Sample OCR.

The we did a basic OCR for the scanned files. Here is a sample output from OCR. It is noticeable that there is a mismatch between recipes and ingredients.

Data digitalization

Template output of the digitization

Data processing

In our project, we will extract the following information from the recipes:

• quantity: the amount of the ingredient
• unit: the metric of the ingredient
• ingredient: the entity appeared in the recipes

Units

• Spoons: 'cuil. à café', 'cuil. café', 'cuil. à soupe', 'cuil. soupe', 'petite cuil.', 'grande cuil.', 'cuil.',
• Glasses: 'petit verre', 'verre à liqueur', 'verres à liqueur', 'verres', 'verre', 'tasses', 'tasse',
• Bottles: 'bout.', 'bouteilles', 'bouteille',
• Containers:'g rande boîte', 'boîtes', 'boîte', 'tubes', 'tube',
• Spices & Aromatic plants: 'gousses', 'gousse', 'branches', 'branche', 'bâtons', 'bâton', 'pincée',
• Meat related: 'membres', 'membre', 'tronçons', 'tronçon', 'tranches', 'tranche',
• Standard measures: 'litres ', 'litre ', 'cl ', 'dl ', 'kg ', 'g ' , 'l'

Categories

• 'Viande': ['viande', 'oie', 'canard', 'oiseau', 'lard', 'bœuf', 'veau', 'poule', 'poulet', 'poularde', 'volaille', 'porc', 'caille', 'canard', 'caneton', 'mouton', 'cochon', 'coq', 'chevreuil', 'lièvre', 'levraut', 'lapin', 'faisan', 'gibier', 'jambon', 'chorizo', 'cervelas', 'agneau', 'escargot', 'grenouille'],
• 'Poisson': ['poisson', 'brochet', 'carpe', 'morue', 'lamproie', 'lotte', 'maquereau', 'omble', 'rouget', 'sardine', 'thon', 'truite', 'anchois', 'anguille', 'merlan', 'sole', 'barbue', 'turbot', 'raie', 'perche', 'saumon', 'colin', 'goujon', 'loup', 'congre', 'rascasse', 'grondin', 'merlu', 'merluza', 'hareng', 'alose', 'brême'],
• "Fruit de mer": ['crevette', 'langouste', 'moule', 'écrevisse', 'palourde', 'homard', 'chiperon', 'seiche', 'huître', 'coquille', 'poulpe'],
• 'Alcool': ['alcool', 'bière', 'vin', 'cidre', 'fine', 'liqueur'],
• "Plante aromatique": ["bouquet garni", 'ail', 'anis', 'aromate', 'angélique', 'basilic', 'persil', 'sarriette', 'cerfeuil', 'ciboule', 'ciboulette', "clou de girofle", "clous de girofle", 'girofle', 'cive', 'câpre', 'estragon', "feuille de vigne", "fines herbes", 'laurier', 'menthe', 'pissenlit', 'romarin', 'thym'],
• 'Epice': ['cannelle', 'coriandre', 'curry', 'safran', 'poivre', 'sel', 'moutarde', 'muscade', 'paprika', 'piment', 'sauge', 'serpolet', 'épices'],
• "Produit laitier": ['lait', 'crème', 'fromage', 'gruyère', 'parmesan'],
• 'Légume': ['artichaut', 'asperge', 'aubergine', 'bette', 'betterave', 'cardon', 'chou', 'cornichon', 'courgette', 'cresson', 'céleri', 'fenouil', 'légume', 'navet', 'panais', 'poireau', "pomme de terre", "pommes de terre", 'potiron', 'rave', 'salade', 'tomate', 'échalote', 'épinard'],
• 'Fruit': ['abricot', 'banane', 'cerise', 'coing', 'fraise', 'framboise', 'groseille', 'raisin', 'olive', 'orange', 'pomme'],
• 'Agrume': ['citron', 'cédrat', "fleur d'oranger", "fleurs d'oranger"],
• 'Céréale': ['farine', 'pain', 'pâte', 'riz'],
• 'Légumineuse': ['févette', 'haricot', 'pois'],
• 'Fruit sec': ['amande', 'noix', 'noisette'],
• 'Champignon': ['champignon', 'truffe', 'cèpe', 'girofle', 'morille', 'levure', 'oronge', 'duelle']

Region2SubRegion

• "Paris, Ile-de-France, Val de Loire": ['Paris', 'Ile-de-France', 'Orléans', 'Touraine'],
• "Pays de l’Ouest": ['Anjou', 'Bretagne', 'Poitou Vendée', 'Charentes'],
• "Sud-Ouest & Pyrénées": ['Bordelais', 'Gascogne', 'Pays Basque', 'Roussillon', 'Périgord', 'Languedoc'],
• "Sud-Est & Méditérannée": ['Provence', 'Nice', 'Corse', 'Dauphiné', 'Savoie', 'Lyon', 'Auvergne', 'Limousin'],
• "Bourgogne, Champagne, Bresse, Franche-Comté, Alsace, Lorraine": ['Bourgogne', 'Champagne', 'Bresse', 'Franche-Comté', 'Alsace', 'Lorraine'],
• "Nord & Normandie": ['Nord', 'Normandie']

Links

Github repository: Historical Cookbook

Future work

• Build a search engine that would display the recipes and add filters to search them by name, region or ingredients

• User-friendly interface to visualize the results of the analysis

• Comparison with other cookbooks from different periods or different countries