swedish-ocr-correction

Swedish OCR correction

<!-- Provide a quick summary of what the model is/does. -->

This model corrects OCR errors in Swedish text.

Try it!

• On short texts in the inference widget to the right ->
• On files or longer texts in the demo

Model Description

This model is a fine-tuned version of byt5-small, a character-level multilingual transformer. The fine-tuning data consists of OCR samples from Swedish newspapers and historical documents. The model works on texts up to 128 UTF-8 bytes (see Length limit).

<!-- ### Model Description--> <!-- Provide a longer summary of what this model is. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]-->

Training Data

The base model byt5 is pre-trained on mc4. This fine-tuned version is further trained on:

• Swedish newspapers from 1818 to 2018. Parts of the dataset are available from Språkbanken Text: Swedish newspapers 1818-1870, Swedish newspapers 1871-1906.
• Swedish blackletter documents from 1626 to 1816, available from Språkbaknen Text: Swedish fraktur 1626-1816

This data includes characters not used in Swedish today, such as the long s (ſ) and the esszett ligature (ß), which means that the model should be able to handle texts with these characters. See for example the example titled Long-s piano ad in the inference widget to the right.

Usage

Use the code below to get started with the model.

from transformers import pipeline, T5ForConditionalGeneration, AutoTokenizer

model = T5ForConditionalGeneration.from_pretrained('viklofg/swedish-ocr-correction')
tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer)

ocr = 'Den i HandelstidniDgens g&rdagsnnmmer omtalade hvalfisken, sorn fångats i Frölnndaviken'
output = pipe(ocr)
print(output)

Length limit

The model accepts input sequences of at most 128 UTF-8 bytes, longer sequences are truncated to this limit. 128 UTF-8 bytes corresponds to slightly less than 128 characters of Swedish text since most characters are encoded as one byte, but non-ASCII characters such as Å, Ä, and Ö are encoded as two (or more) bytes.