Quartiers Livres / Booking Paris

Goal of the project

This project aims at presenting an interactive map of the the book industry in the middle of the XIX century based on Pretod's yearbook of Paris typographers. At the end of the projet, one will be able to see the different places (workshops, bookshops etc.) at their approximate location in the years 1840.

Data Presentation

Original sources from BnF

Our primary source for this work is the yearbook. We analyse the issues that were edited between 1844 and 1847. In the book are present the names and the address of different type of business related to book.

Our second main source, is a Paris map from 1846 found on the BnF website. We decided to choose that one because it was the most recent map in the first sampling we did. Which we thought would be the best solution to superpose with a recent mapping service.

Project process description

Extraction

Our books had already been "OCRized", and were available as text files for download. The quality of the OCR seemed to be good enough to begin with the extraction of the information. The text we kept for analysis contained only two distinct form of layout / structure, one for the printers and one for all the other jobs. For each year, we created a single file for every job. Even after long checks, we had a different number of files per year. With regular expression, it was possible to split between the names and the adresses of the people of the yearbook. The first specificity of the formats, where from the printer data part. In addition to the name of the place, the name of the "prote" also appeared if there was one. The second was the inconsistent presence of specific descriptions about was was printed in the place or general informations. We also stored all this inconsistent data. For data quality reason and clarity reasons we decided to only use the NAME and ADRESS field. We rapidly got all our Data into tables.

Adresses coordinates

During the middle of the XIX century, Paris changed in its inner structure. Some address would then change between now and then. Due to this, the use of tool like GeoPy. But thankfully we got a CSV document from the DHLAB in order to get the coordinated of adresses of the street names that we extracted from the yearbook. But, as the performance was really low, we decided to opt for the google option.

Maps

We selected an old map from paris from 1850 on the BNF website. This map was then georeferenced with the online tool [1]. This map will be used to place all our adress points on our final project website.

General aim of the project

This project aim to present the different neighbourhoods where people in the book industry where located in the middle of the XIX century. Starting from Pretod's yearbook of Paris typographers, an interactive map has been created in order to visualise the different places where workshops, bookshops etc. were located in the years 1840.

Data Presentation

Original sources from BnF

Our primary source for this work is the yearbook. We analyse the issues that were edited between 1844 and 1847. In the book are present the names and the address of different type of business related to book.

Our second main source, is a Paris map from 1846 found on the BnF website. We decided to choose that one because it was the most recent map in the first sampling we did. Which we thought would be the best solution to superpose with a recent mapping service.

Project process description

Extraction

Our books had already been "OCRized", and were available as text files for download. The quality of the OCR seemed to be good enough to begin with the extraction of the information. The text we kept for analysis contained only two distinct form of layout / structure, one for the printers and one for all the other jobs. For each year, we created a single file for every job. Even after long checks, we had a different number of files per year. With regular expression, it was possible to split between the names and the adresses of the people of the yearbook. The first specificity of the formats, where from the printer data part. In addition to the name of the place, the name of the "prote" also appeared if there was one. The second was the inconsistent presence of specific descriptions about was was printed in the place or general informations. We also stored all this inconsistent data. For data quality reason and clarity reasons we decided to only use the NAME and ADRESS field. We rapidly got all our Data into tables.

Adresses coordinates

During the middle of the XIX century, Paris changed in its inner structure. Some address would then change between now and then. Due to this, the use of tool like GeoPy. But thankfully we got a CSV document from the DHLAB in order to get the coordinated of adresses of the street names that we extracted from the yearbook. But, as the performance was really low, we decided to opt for the google option. We present the results in an other part of the wiki.

Performance assessment

OCR

We started with OCR between pages 20 and 25 of 1844 (20 addresses): number of correctly transcribed addresses/total number of addresses.

OCR_addresses = 11/20

We now continue with the performance of the OCR for the recognition of NOMS. It is assumed that the performance is independent of that of the addresses, since the position in the text is different and the names are not on several lines like the addresses, which seems to be a problem.

The protests have the same typography as the names on the book, so it can be assumed that their recognition rate is the same as for the names of printers.

We can now statistically evaluate the chances of having a perfect result (good addresses + good names) or a totally false result, assuming the independence of the recognition of addresses and names and without considering protests. perfect_chances = 11/20 * 10/21 perfect_chances 0.2619047619761904047619

false_chances = 9/20 * 11/21 = 0.2357142857142857

false_chances + perfect_chances = 0.49761904761976190476

In almost half of the cases, we therefore have a result whose value on both is correct.

If we now take into account the protests: perfect_protect_chances = 11/20 * 11/20 * 10/21 1/ perfect_proof_chances = 6.942148760330578

In this case, we are close to 1/7th of perfect values

The most important value for OCR performance to define performance will be retained: address recognition, from 11/20.

Let's now look at the performance with which our algorithm clearly identifies names, comments and addresses and ranks them correctly in the dataframe. To do this, we manually look at each line of the text file and the database to analyze a small number of data. Although this performance is dependent on the quality of the OCR, it is independent of the previous measurement which only assessed the accuracy of address transcription and not the general segmentation. The measurement is carried out on 40 printing plants from 1844 starting on page 26 of the Prétod directory. text_to_DF = 36/40

From address to GPS coordinates

Identification by address directory of old Paris

We see in the test_GEO.csv that only 29 lines were written following the matching in the old Paris address directory, i.e. a ratio of 29/6203, less than 1/200.

Among these 29 lines, we see that only 13 are actually address matches, and that the others contain errors in their address. 13/6203 = 0.002095760116072868

Identification by google maps API

Note that the google API always returns GPS coordinates, regardless of the quality of the input. Thus, to evaluate the performance of the API alone, it must be taken into account that 9/20 of the addresses sent to the API are false (poorly recognized), and thus only 11/20 of the errors in the GPS coordinates provided by the API can be attributed to the poor recognition of the address by the API.

To concretely evaluate the performance of the API, we will be able to compare the 13 certain values from the directory with the coordinates returned by the API. We can assume that they are accurate if the GPS coordinates of the API are equal to those of the directories to within 50 meters in absolute value in all directions, i. e. an angular deviation in latitude or longitude of max.....

Web Application

Developpement

For the development of the application we used the Flask framework. With this tool it was easier for us to quickly create a one page website. The idea of the website is to create a small narrative around our work, as if someone wanted to publish a book in the 1840' and had access to this site.

Project calendar

Deadline	Focus
25.11	Mapping a minimal set of data
2.12	Prototype of web application, Database ready from Annuaire Pretod, Getting descriptions ready for the map, Extract sizes of printers workshop
9.12	Implementation of search result and visualisation of book neighboorhood in the city Adding data from publishers from Bnf
16.12	Improvement of UI and final app design

With the troubles we encountered with the data from the annuaire we finally didn't use the extra information about the size of the workshop, nor the data about every book published by the workshop.

Link

Github project's page