France: Exploring Historical Cookbooks: Difference between revisions

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===Data visualization===
===Data visualization===
 
[[File:Cooc.png|400px|right|thumb| Cooccurrence of categories.]]
<gallery caption="" mode="packed-hover" heights="580px" class="center">
Image: Cooc.png | Cooccurrence of categories
</gallery>
 
[[File:France.png|400px|right|thumb| Map of France.]]
[[File:France.png|400px|right|thumb| Map of France.]]



Revision as of 19:26, 21 December 2022

Introduction

This project is an exploration of historical French cookbooks containing recipes from the 19th century. Through analyzing these cookbooks, we explore the most frequently used ingredients and food categories by region. We also examine the cooccurrence of ingredients and food categories.

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

Milestone 1: Project proposals

  • Find multiple cookbooks in French or English from different times.
  • Prepare project proposals.
Week 4
  • Compare different cookbooks, consider the OCR scan and think of possible research questions.
  • Discuss the goal and implementation of the project.
Week 5
  • Decide on one French cookbook.
  • Scan the physical book page by page.
  • Sanitize the initial dataset.
Week 6-7
  • Separate .png files by subregions.
  • Perform the OCR of each page.
  • Start to construct the dataset.
Week 8-9

Milestone 2: Midterm presentation

  • Construct dataset and think of data structures to store in an optimal way the information.
Week 10
  • Set up the GitHub repository.
  • Finish the creation of the dataset.
Week 11
  • Go through the dataset and perform some changes to facilitate the data processing.
  • 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

Milestone 3: Final presentation

  • Prepare the final presentation
  • Finish the Wikipedia page

Methodology

Data 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 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


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

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
Beurre.png
2) Sel 167
Sel.jpg
3) Poivre 146
Poivre.jpeg
4) Œufs 101
Œufs.jpeg
5) Oignons 95
Oignons.jpeg
6) Farine 89
Farine.png
7) Persil 82
Persil.png
8) Ail 76
Ail.jpeg
9) Vin balanc 69
Vin balanc.jpeg
10) Bouquet garni 46
Bouquet garni.png

Data visualization

Cooccurrence of categories.
Map of France.

Discussion and limitations

Like many other research, this project has its limitations. For example, in the data processing part, data analysis part,

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

Links

Github repository

Scanned book

OCR result