France: Exploring Historical Cookbooks: Difference between revisions
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!scope="col"|Data Processing | !scope="col"|Data Processing | ||
!scope="col"|Data Analysis | !scope="col"|Data Analysis | ||
!scope="col"|Research Questions | |||
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!scope="row"|Week 3 | !scope="row"|Week 3 | ||
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Collect multiple historical cookbooks in French or English | |||
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Prepare project proposals | |||
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!scope="row"|Week 4 | !scope="row"|Week 4 | ||
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Compare the different cookbooks, considering the layout extraction & OCR | |||
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Discuss the objectives of the project | |||
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* Scan the physical book page by page. | * Scan the physical book page by page. | ||
* Sanitize the initial dataset. | * Sanitize the initial dataset. | ||
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* Perform the OCR of each page. | * Perform the OCR of each page. | ||
* Start to construct the dataset. | * Start to construct the dataset. | ||
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* Construct dataset and think of data structures to store in an optimal way the information. | * Construct dataset and think of data structures to store in an optimal way the information. | ||
'''Milestone 2: Midterm presentation''' | '''Milestone 2: Midterm presentation''' | ||
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* Set up the GitHub repository. | * Set up the GitHub repository. | ||
* Finish the creation of the dataset. | * Finish the creation of the dataset. | ||
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!scope="row"|Week 11 | !scope="row"|Week 11 | ||
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* Go through the dataset and perform some changes to facilitate the data processing. | * Go through the dataset and perform some changes to facilitate the data processing. | ||
* Create categories for ingredients. | * Create categories for ingredients. | ||
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* Finish the Wikipedia page | * Finish the Wikipedia page | ||
'''Milestone 3: Final presentation''' | '''Milestone 3: Final presentation''' | ||
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Revision as of 00:27, 22 December 2022
Introduction
Cuisine has an important place in the cultural heritage of France. In the 21st century, the great classics of French cuisine can be found in starred restaurants of most cities of France and even all around the world. But above all, French cuisine owes its current prestige to the different regional cuisines that were developed over several hundred years, taking advantage of the geographical and cultural specificities of each region.
This is at least the point of view of Mr. Curnonsky who travelled the regions of France throughout his life at the beginning of the 20th century in search of the traditional regional recipes that are the pillars of the French cuisine we know today. His book Recettes des Provinces de France written in 1962 [Figure 1] gathers many traditional recipes collected by himself all around France.
At a time when all knowledge is shared online on the web, it has become easy to obtain information on the history of French cuisine or even many contemporary recipes. However, a significant amount of knowledge and culinary practices are still stored in books that are much more difficult to access. This knowledge would benefit from being digitalized both to share it with the largest number of people, but also to take advantage of the latest computational techniques to perform more in-depth analyses.
This project is hence an exploration of a historical French cookbook. From the physical book to a clean structured dataset, our main focus is on the digitalization of a historical cookbook and its challenges. In addition to that, we use the collected knowledge to extract analyses to better understand the French cuisine of the previous century. We use the cookbook from Mr. Curnonsky mentioned before as an example to answer our research questions.
Research Questions
More specifically, we aim at answering the following research questions:
- What are the steps and difficulties when digitalizing an old cookbook?
- What knowledge can be extracted from a cookbook and what information can it provide about the culture and practices of the region at that time?
- From Mr. Curnonsky's cookbook, what can we say about the regional cuisine of France in the early 20th century?
Project Plan and Milestones
Date | Data Collection | Data Processing | Data Analysis | Research Questions |
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Week 3 |
Collect multiple historical cookbooks in French or English |
Prepare project proposals | ||
Week 4 |
Compare the different cookbooks, considering the layout extraction & OCR |
Discuss the objectives of the project | ||
Week 5 |
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Week 6-7 |
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Week 8-9 |
Milestone 2: Midterm presentation |
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Week 10 |
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Week 11 |
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Week 12 |
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Week 13 |
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Week 14 |
Milestone 3: Final presentation |
Methodology
Data collection and digitalization
For a start, we scanned a physical French cookbook. This one is from the 19th century and has ingredients listed on the margin of the page.
Then 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 processing
In our project, we will extract and construct 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
- category: the major category the ingredient belongs to
Units
Type | Unit |
---|---|
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
We map different ingredients to several major categories
Category | Ingredient |
---|---|
Viande (Meat) | 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 (Fish) | 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 (Sea food) | crevette, langouste, moule, écrevisse, palourde, homard, chiperon, seiche, huître, coquille, poulpe |
Alcool (Alcohol) | alcool, bière, vin, cidre, fine, liqueur |
Plante aromatique (Aromatic plant) | 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 (Spicy) | cannelle, coriandre, curry, safran, poivre, sel, moutarde, muscade, paprika, piment, sauge, serpolet, épices |
Produit laitier (Diary product) | lait, crème, fromage, gruyère, parmesan |
Légume (Vegetable) | 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 (Fruit) | abricot, banane, cerise, coing, fraise, framboise, groseille, raisin, olive, orange, pomme |
Agrume (Citrus) | citron, cédrat, fleur d'oranger, fleurs d'oranger |
Céréale (Cereal) | farine, pain, pâte, riz |
Légumineuse (Legume) | févette, haricot, pois |
Fruit sec (Nut) | amande, noix, noisette |
Champignon (Mushroom) | champignon, truffe, cèpe, girofle, morille, levure, oronge, duelle |
Region
We map subregions to 6 major regions in 19th-century France.
Region | Subregion |
---|---|
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 |
Data analysis and visualization
Dataset Overview
We have a total of 352 different recipes from 30 regions, 6 subregions.
Top 10 most used ingredients.
Rank | Ingredient | Number of occurrences | Picture |
---|---|---|---|
1) | Beurre | 180 | |
2) | Sel | 167 | |
3) | Poivre | 146 | |
4) | Œufs | 101 | |
5) | Oignons | 95 | |
6) | Farine | 89 | |
7) | Persil | 82 | |
8) | Ail | 76 | |
9) | Vin balanc | 69 | |
10) | Bouquet garni | 46 |
Region Analysis
From this graph, we could see that "Plante aromatique" and "Epice" are frequently used by all the six major regions while "Fruit sec" is the least frequently used one.
Subregion Analysis
Co-occurrence Analysis
We could see that "Plante aromatique" and "Epice" appear together a lot, then they appear together with "Viande", "Légume", and "Céréale".
Discussion and limitations
Like many other research, this project has its limitations. For example, in the data analysis part, it was a roughly count of categories and we did not take quantity into account.
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