End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of Moccia Code

Abstract

The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and for further automatic processing. We propose a learning-free method for automatically aligning the transcription to the document image. The method receives as input the digital image of the document and the transcription of its content and aims at linking the transcription to the corresponding images within the page at the word level. The method comprises two main original contributions: a line-level segmentation algorithm capable of detecting text lines with curved baseline, and a text-to-image alignment algorithm capable of dealing with under- and over-segmentation errors at the word level. Experiments on pages from a 17th-century Italian manuscript have demonstrated that the line segmentation method allows one to segment 92% of the text line correctly. They also demonstrated that it achieves a correct alignment accuracy greater than 68%. Moreover, the performance achieved on widely used data sets compare favourably with the state of the art.

Proceedings of the 4th International Conference on Historical Cryptology HistoCrypt 2020

“…Further work combine Hidden Markov Models (HMMs) with the Viterbi algorithm for the word imageto-text alignment phase (Rothfeder et al, 2006;Feng and Manmatha, 2006;Toselli et al, 2007;Fischer et al, 2011), with the latter also dealing with writing-transcript inconsistencies. Good word segmentation is usually difficult to obtain, so this family of techniques is usually combined with some form of correction algorithm that prevents catastrophic failure cases (De Gregorio et al, 2022;De Gregorio et al, 2023).…”