Researchers have been testing various Optical Character Recognition (OCR) models on historical book pages to assess their accuracy. The top-performing model achieved a 97.6% character accuracy rate, which is sufficient for training language models but not suitable for scholarly transcriptions. A collaborative project, FineBooks, aims to address this limitation by providing more accurate OCR text at a lower cost. The goal is to make high-quality historical text digitization more accessible and affordable for researchers and scholars. This improvement in text accuracy matters because it will enable more accurate and reliable language model training and scholarly research.
Efforts Underway to Improve Accuracy of Historical Text Digitization
Original source
Read the full story at The Decoder →This is an original summary written by Rouagent News. The reporting belongs to The Decoder. Follow the link for their full article.