Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs
This work addresses the poor OCR quality hindering the exploitation of historical commentaries in literary scholarship, though it is incremental as it compares existing methods on a specific domain.
The paper evaluated two OCR pipelines for digitized 19th-century classical commentaries, finding that Kraken + Ciaconna achieved a lower character error rate (7% vs. 13%) for sections with dense Greek text, while Tesseract/OCR-D was slightly better for Latin script sections (8.2% vs. 8.4%).
Together with critical editions and translations, commentaries are one of the main genres of publication in literary and textual scholarship, and have a century-long tradition. Yet, the exploitation of thousands of digitized historical commentaries was hitherto hindered by the poor quality of Optical Character Recognition (OCR), especially on commentaries to Greek texts. In this paper, we evaluate the performances of two pipelines suitable for the OCR of historical classical commentaries. Our results show that Kraken + Ciaconna reaches a substantially lower character error rate (CER) than Tesseract/OCR-D on commentary sections with high density of polytonic Greek text (average CER 7% vs. 13%), while Tesseract/OCR-D is slightly more accurate than Kraken + Ciaconna on text sections written predominantly in Latin script (average CER 8.2% vs. 8.4%). As part of this paper, we also release GT4HistComment, a small dataset with OCR ground truth for 19th classical commentaries and Pogretra, a large collection of training data and pre-trained models for a wide variety of ancient Greek typefaces.