LaroplanOCR

Swedish primary school curricula (Läroplaner för grundskolan) in digital format.

We use optical character recognition (OCR) to transform curricula in image format into text. For each curriculum we construct datasets at the paragraph, sentence, and word levels.

Using the Datasets

In ./analysis/output/ you will find the following for the Läroplan for year YYYY:

• lgrYYYY_counts.csv: counts of individual words
• lgrYYYY_paragraphs.csv: individual paragraphs with page of appearance
• lgrYYYY_sentences.csv: individual sentences with page and paragraph of appearance

In the folder ./example/ we provide an illustration on how to use the data to search for, and plot, counts of a desired set of words.

Using the Code

Prerequisites

You may want to compile some or all of the code yourself. To do so, you need the following prerequisites.

• A python compiler version 3.8 or above.

  • We recommend installing Anaconda to get Python
  • You will need to install the dependencies in requirements.txt. See quick start below

• git for version control.

  • And git-lfs for versioning large files

• The software Tesseract-OCR:

  • For Windows, install following steps here. For Mac, follow steps in section "Installing Tesseract on Mac" here
  • Make sure to add "Swedish" when installing, in "Additional language data (download)"
  • Add the Tesseract-OCR installation folder to your path

• For Mac, you also need to install poppler for Mac in order to use the python package pdf2image, see here.

Users may notice small differences in output across machines due to the way images are processed.

Repository structure

• ./run.py is a python script used to build the entire repository.

• ./raw/ contains pdf files of Swedish curricula (Läroplaner).

  • /orig/ contains curricula in pdf format
  • /docs/ contains documentation

• ./derived/ contains code that runs the OCR and cleans its output.

  • /code/make_images.py transforms curricula in pdf format to separate jpg files, the jpg files are not included in the repo
  • /code/ocr.py reads the jpg files into text
  • /code/clean.py cleans the text files produced by the OCR

• ./analysis/ constructs datasets from the digitized text of each curricula.

  • /code/make_data.py transforms the cleaned text files into paragraph- and sentence-level datasets
  • /code/count.py counts all appearances of words in each curricula

Each folder hosts an /output/ subfolder where output from each script is saved.

Quick start

1. Clone the repository to your local machine.

   # Using SSH
   git lfs clone git@github.com:JMSLab/LaroplanOCR.git
   # Using HTTPS
   git lfs clone https://github.com/JMSLab/LaroplanOCR.git

2. Install dependencies. From the root of the repo run:

   pip install -r requirements.txt

3. To compile the entire project, open the command-line and run

   python run.py

   You may also compile specific steps of the pipeline. For example, python derived/code/make_images.py will transform the pdf files of the curricula into jpg files.

Citations

• Hermo, Santiago, Christian Lundqvist, Miika Päällysaho, David Seim, Jesse M. Shapiro, and Stina Trollbäck. 2022. LaroplanOCR. Code and data repository at https://github.com/JMSLab/LaroplanOCR.
• Hermo, Santiago, Miika Päällysaho, David Seim, and Jesse M. Shapiro. 2022. Labor Market Returns and the Evolution of Cognitive Skills: Theory and Evidence. The Quarterly Journal of Economics, Volume 137, Issue 4, November 2022, Pages 2309–2361. DOI: https://doi.org/10.1093/qje/qjac022.

Acknowledgments

We thank our dedicated research assistants for contributions to this project.