Authors: Tony Montes, Rubén Manrique, Laura Manrique

First, a raw version of the dataset is on a /raw folder within the /data folder. This folder contains all the folders with the JSON files extracted from the Azure's OCR for each Newspaper; for example, the folder /data/raw/PD168_El_oso_results contains a set of JSON files with the format:

{
	"metadata": {
		"id": "PD168",
		"newspaper": "El oso",
		"year": 1845,
		"city": "Lima, Perú",
		"file": "1",
		"page": "page_0"
	},
	"contexts": [
		{
			"id": 0,
			"text": "La publicacion del Oso se harà dos veces cada se mana, y constará de un pliego en cuarto ; ofreciendo à mas sus redactores, dar los gravados oportunos, siempre que loexija el asuntode que trate. Redactado por un Num. 8. TEMA del Periodico. POLITICA MILITAR. OCTAVA SESION. Abierta la sesion á las dore y un minuto de la noche , 25 de Febrero de 1845 , con asistencia de todos los Señores Representantes, se leyó y aprobó la acta de la Asamblea anterior , ménos en lo tocante à la torre del Convento de Santo Domingo, punto que quedó para ventilarse en mejor ocasion. En seguida se dió cuenta de una nota del Ejecutivo , referente à que urjía la necesidad de organizar un Ejército ; pues decia el Excmo. Decano: - \"Un poder sin bayonetas vale tanto como un cero puesto á la izquierda.\" ",
			"bounding_box": [43.0, 159.0, 496.0, 1496.0],
			"center": [269.5, 827.5]
		}
    ]
}

The first step is to structure all the JSON files within a parquet file, this is done with the structuring.py file, that reads all the JSONs and structures them.

The cleaning step is done in the cleaning.ipynb notebook, which contains step by step all the substeps:

1. Remove duplicates and empty texts.
2. Filter out rows where 50%+ of the characters are non-alphabetic.
3. Remove all the rows with 4 or less tokens.

Also, an initial version of a tokenizer (trained from the BETO pre-trained tokenizer in Spanish) is saved in this step

Finally, for the OCR correction with LLM there are 3 notebooks that must be ran in order:

1. get-llm-responses.ipynb: Get the LLM responses from the correction requests for each text in the cleaned dataset.
2. get-corrections.ipynb: From the LLM responses, find each correction made individually in order to later classify it between OCR error, surface form or none. Also, in this step, an initial OCR-correction detection is done for the most basic case.
3. classify-corrections.ipynb: Classify the corrections made by the LLM in the previous step, based on hardcoded rules and exceptions.