3.1 General aspects of the corpus

Corpus construction theory Borrowing the terminology from the linguistic domain (Bauer & Aarts, 2000) where data construction methods have long been examined, evaluated, and reconsidered, we shall examine the following methodological aspects. Contrary to the notion of “sampling” which is, by definition, a random selection procedure, “corpus construction” implies a systematic selection of materials that obey a specific rationale, where its efficiency depends on the research question. “Representative sampling” is where these two approaches converge. Sampling secures efficiency in research by providing a rationale for studying only parts of a population without losing information. Its key feature is “representativeness” of the system in question. Sampling criteria and focal variables correlate. In HTR for medieval manuscripts, “representativeness” was approached in terms of the medieval handwritten Latin language’s characteristics as a system comprised of abbreviations, ligatures, and punctuation signs alongside graphemes. Different genres, scripts, and their degrees of formality served as instances of this system.

Document sampling strategy From the three registers making up the construction of a qualitative corpus according to Bauer and Aarts (2000), namely channel, domain, and function, only the first parameter is constant in our case: the sample represents exclusively the written Latin language while giving room to texts of multiple functions addressed to different audiences belonging to various genres (while not aiming at exhaustiveness at this stage). The corpus construction can be regarded as a cyclical process: it has not been entirely determined a priori but rather evolved, bearing in mind the logic of complementarity regarding the already existing datasets. Estimated abbreviation rate and use of specific characters, known genres and scripts were implemented to compensate for what was thought to be missing from the network of the corpus and the corpus itself in order to make it as “representative” as possible. HTR engines are language agnostic, but the same cannot be said for the resulting models, which means that it depends on the representativeness of the sample to determine whether a model will work on “similar” or “out-of-domain” documents.

Three distinctive selection processes have been applied in our case:

1. The first set of documents was selected purely on their linguistic feature, their readability, and their availability as both digitized manuscripts and editions which could be found either online or in local libraries. It led to the inclusion of classical texts such as Seneca’s Medea. Script was not taken into account.
2. In a logic of complementarity, the second part of the corpus was dictated inversely by content. More specifically, given the relative absence of ligatures and abbreviations in classical texts, we chose documents that would display a higher degree of abbreviations. This both induced or led to a genre selection process, specifically for medical and scholastic data. At the same time, script diversity was added to the consideration and came naturally as a sort of by-product.
3. Finally, as we wanted to test Kraken models, we sought a transcription project that would provide us with data that would help us evaluate our own. This led to the alignment of the Eichenberger and Suwelack (2021) dataset, produced in the context of a transcribathon in Berlin and containing genres new to our corpus (Book of Hours, Psalms, etc.).

Quantitative aspects of the corpus Corpus size depends largely on the subjective criteria and resources of each project and little can be said as a general rule: one needs to consider the limitations that stem from the effort put into producing the corpus, the budget available, the number of representations one wants to characterize, and some minimal and maximal requirements (in our case the quota for the production of an efficient HTR model). Building a turn-key HTR model applicable to as large a range of unseen manuscripts as possible is undoubtedly the end goal. With the production of ground truth being expensive but with increasingly more open-access models available to the public, the challenge is finding the right combination of GTs (either to create a model from scratch or to fine-tune an existing one) that yield the best results. This is where considerations of size and variety enter the discussion and affect directly the quantitative corpus construction strategy.

More specifically, while conducting an experiment on Caroline Minuscule OCR models, Hawk et al. (2018) conclude that “relative preponderance”⁴ in small training pools was a considerably more important factor than that of size, which inversely impacts the accuracy of the models resulting from larger training pools. A careful conclusion would be that a specific combination of manuscripts can yield exceptional results, even though the reasons behind such results or the criteria for the respective manuscripts to be combined are not entirely clear yet. This means that quantity-wise we sought to find a balance between the diversity and size of the GT, always making sure that the ground truth yields an efficient model for individual manuscripts on the training set. Training and fine-tuning experiments conducted by Pinche showed that a specialized model per script isn’t always necessary, but the variety of the training set increases its robustness. Therefore, the size of each GT belonging to the training set was limited to 5 pages per script variation (depending on the density of the layout),⁵ examining whether this balance can contribute to the production of generic models.⁶

Segmentation vocabulary: SegmOnto With the emergence of efficient layout analyzers and easy-to- use interfaces, the need for efficient segmentation models increases (as does the need for large amounts of data) based on the aggregation of heterogeneous documents. Alongside text recognition, eScriptorium allows for layout annotation using ontologies and controlled vocabularies. For this, researchers need to agree on a limited common vocabulary and share common practices to facilitate the interoperability of their ground truth.

In order to identify the different areas of the document and the type of lines present on the page as well as to characterize them from a codicological point of view, we decided to implement the controlled vocabulary SegmOnto (Gabay, Camps, Pinche, & Jahan, 2021). SegmOnto was born out of the need for a small/restricted common ontology based on existing standards for the description and analysis of document layout, ranging from content categorization to text recognition, mainly addressing the case of manuscripts and early printed books.

SegmOnto has already been implemented in several projects led by Pinche and connected to the CREMMALab project such as Gabay et al. (2022), resulting in segmentation models mainly for late medieval manuscripts and early prints.⁷ As per the CREMMA Medii Aevi dataset, the documents present two kinds of layout: multi-columns and singular columns, for which lines are most often long, except for the Psalms and Book of Hours. SegmOnto offers multiple levels of description, of which only the first is completely standardized, as the second is intended for custom refinement and the third for local and document-based differentiation. For the purposes of the project, only the first level of SegmOnto has been utilized, such as MainZone for columns and MargintextZone for marginalia.

Pinche’s Transcription Guidelines Pinche (2022c) stressed that HTR was an answer to the need for scientific projects to acquire textual data either to undertake editions or to constitute large corpora. Her guidelines address the need to establish principles common to projects dealing with the transcription of manuscripts in order to:

• build shareable, reusable, and durable ground truth data sets;
• produce robust generic models, reusable in “out-of-domain” manuscripts;
• minimize the collective cost, including that of training people;
• build GT that seeks to optimize the learning space of HTR models.

Pinche has privileged a graphemic transcription, which reproduces graphemes, i.e. a canonical form for each character, instead of a graphetic one, which tries to reproduce each variation of a letter (such as ſ and s).⁸ Pushing the imitation too far through a graphetic approach induces a risk of making the transcription harder to complete (as it requires technical skills to recognize differentiated shapes of characters), harder to make uniform (specifically as more annotators are to participate in a dataset) and potentially unusable for HTR (as it might introduce more characters and ultimately noise for HTR engine to learn). Therefore, in cases where functional signs have more than one graphetic manifestation but essentially the same function, they could be represented by the same sign: for example, for every manifestation of the paragraph sign, we opt for the pilcrow sign “¶” (U+00B6) on every occasion, instead of several variations such as “” (U+F1E1).⁹ In the context of the guidelines, we set up a list of allowed characters and a list of common and rare cases (such as Table 2 and 3).

On the topic of abbreviations, resolving them produces specific difficulties for HTR engines, as it leads them to learn more about the language than they originally intended.¹⁰ Abbreviations are not resolved in our dataset, as this constitutes rather an interpretative act linked to the specificity of each document. It is not the same as a textual prediction and it could prove to be detrimental to the extension of an HTR model in the long term. Pinche’s graphemic approach without abbreviation resolution simplifies the interpretation step of the text, and in turn, the reduction of characters diversity ultimately smooths both the human transcriber and the HTR engine’s learning curves.

In order to ensure the rigorous application of these guidelines and the homogeneity of the data produced, we introduced quality control to the production and publication workflow. Each manuscript transcription was passed through ChocoMufin (Clérice & Pinche, 2021), using project-provided character translation and control tables.

This software, alongside these tables, allows for each dataset to be both controlled at the character level and adapted to guideline specifications and modifications. It also allows for project-specific transcription guidelines to be translated to a more common one such as CREMMALab’s (Pinche & Camps, 2022).¹¹ This process has been largely used in the first months of the CREMMA Medieval project, as the guidelines were still being drafted. It allowed Pinche to produce or align datasets first and harmonize later, as long as the harmonization was from an upper level of details (closer to graphetic) to a lower level (closer to graphemic).

3.2 Transcription Guidelines for the CREMMA Medii Aevi

The section that follows aims to guide the reader through the transcription norms followed for the Medii Aevi dataset, illustrating the process and the more common and complex cases, especially where new characters have been introduced compared to the CREMMA Medieval dataset.

The project adheres to the general principles laid out by Pinche (Pinche, 2022c, Tables pp. 4–15) concerning the base cases (punctuation, word separation, functional signs, superscript letters, abbreviations, ligatures, and roman numerals). Using the project-provided character conversion table, ChocoMufin controls the transcription and corrects any anticipated error by transforming the character automatically so it conforms to the pre-defined guidelines (data should be used in their post-ChocoMufin converted state as it sometimes corrected mistranscription). However, where the guidelines were not directly addressing the situation (new characters, new types of abbreviations), we positioned ourselves and interpreted the guidelines in light of the situation. Each decision was discussed with the original guidelines’ author.¹²

In general, the main differences that we isolated between the CREMMA Medieval and Medii Aevi datasets, stemming from the language as well as the genre’s own characteristics, are:

1. the dataset bears no accentuated vowels like in the Old French texts (a rare event though for the corpus);¹³
2. no normalization or distinction of u and v was provided, nor of i and j;
3. two variations of con are found, namely the antisigma and the 9-shaped form;
4. a higher diversity of abbreviating character usage and signification;
5. Arabic numerals alongside roman, mostly in scholastic and medical treatises.

Reference marks, functional signs, and punctuation In general, complex medieval punctuation has been simplified as much as possible, with single sign punctuation being reduced to “.” and commas will be rendered as “,”. Double sign punctuation (mainly punctus elevatus and punctus interrogativus) are consistently reduced to “:”. The hyphenation for words that continue to the next line has been marked with a unique “-” (U+002D) sign, following 3.1. Table 2 gives a representative example.

Contractions, Abbreviations, and Ligatures Cappelli (1899) categorized abbreviations into six categories: truncation, contraction, abbreviation marks significant in themselves, abbreviation marks significant in context, superscript letters, and conventional signs. As Pluta (2020) stresses, the six aforementioned categories are not mutually exclusive, but the functional grouping is helpful.

Contractions: A word is abbreviated by contraction when one or more of the middle letters are missing. Such an omission is indicated by one of the general signs of abbreviation, present in both corpora, always following Pinche (2022c). Thus, macrons and generally horizontal lines diacritics over the letter such as tildes are represented by combining horizontal tildes, and any vertical zigzag and similarly shaped forms are simplified into combining vertical tildes. In our corpus, in cases where a macron is extended to more than one letter due to the cursivity of the script, this trait has been reproduced in the transcription, as well as in the case of stacked diacritics, usually in later medieval manuscripts (cf. Table 4), as long as it was a semantic feature and not a decorative one.

Abbreviation marks significant in themselves: “Standard” Abbreviations signs have been preserved as such, like pr(a)e -p̃ (p + combining tilde, p + U+0303), pro - ꝓ (U+A753), hoc - ħ (U+0127), ẜ (s with diagonal stroke, U+1E9C) for secundum or ser-, ꝯ for 9 shaped con/cum (U+A76F), Tironian sign ꝰ for the desinence -us (U+A770), for (t)ur (U+1DD1), and Ꝙ / ꝙ for quod. Absent from the CREMMA Medieval but present in Medii Aevi, the truncated ending -is is transcribed using the character ꝭ (U+A76D). The “inverted c” variation of the preposition con/cum is a good example for the difference of approach between the graphetic and graphematic approach: while using the antistigma (ↄ) is more faithful, it simply is an allograph of the original ꝯ. For -rum, the symbol ꝵ is used rather than the rotunda -rum ꝝ (U+A75D).¹⁴

Abbreviation marks significant in context: The abbreviation for the enclitic -que, or simply -bus or vertical -m in later manuscripts, has been reduced to the semicolon-shaped ; sign (U+F1AC), avoiding the private domain ligature specific (U+E8BF) character but also avoiding confusion with the regular semi-colon.

Conventional signs: a category that includes all signs that stand for a frequently used word or phrase, and they are almost always isolated (cf. Pluta (2020)). First, a rather frequent one, the abbreviation sign for esse is represented by the mathematical operation ≈ (U+2248). The Division sign ÷ is used ubiquitously for the abbreviation sign of est/id est. Tironian et (U+204A, all variations of it, cf. below) is transcribed by ⁊. Etiam can also be found abbreviated by a combination of the Tironian et and the macron symbol (see Table 4).

Ligatures, ie. combinations of more than two letters in one form with the reduction of proclitic and enclitic letters or abbreviating symbols placed above or joined with letters are reduced to their original alphabetical components. Ligatures between letters in cursive scripts such as the ſt (U+FB05) ligature or the two ff (U+FB00) ligature are resolved as -st- and -ff-. For the very frequent quia, the transcription qr has been privileged, avoiding the MUFI sign that belongs to the private domain. More examples are provided in Table 4.¹⁵

Superscripts letters and interlinear additions A standard way of contracting a word is by adding a superscript letter which gives information about the abbreviated sequence. Frequent ones are open a, u, o, or the ending of a word altogether. These were all rendered with the aid of superscript characters (Pinche, 2022c, p. 11). Ergo and igitur are two of the most frequent examples of abbreviations with superscript letters. Letters without any baseline letter are simply represented with the same combining superscript character and space as the supporting baseline character (e.g. “ͣ ͭ”: space + combining a + space + combining t cf. Figure 1).

Superscript letters, alongside abbreviating functions, were sometimes used to render interlinear additions. Missing content or annotations are added in the interlinear space, especially in manuscripts of scholastic and medical content. This was something that was at first a challenge for the transcription process due to segmentation constraints. It can be, at times, impossible to completely differentiate the segmentation masks of two vertically adjacent letters (like the interlinear additions). Therefore, provided that the corresponding combining letter exists and both words can be formulated, no new lines were carved for the interlinear additions. Where this was deemed too complex, interlinear additions were omitted (see Figure 2).

Rare characters and Numerals Referring to corpus construction practices for balanced corpora, Maniaci (1993) stresses that “sporadically attested variables will therefore be preferred to those that appear in all – or almost all – the individuals that are part of the corpus.” Rare characters, a subset of freestanding abbreviation signs, specifically occurring in the Medii Aevi dataset are therefore given special attention (cf. Table 5). In two of the manuscripts, both of medical content, some occurrences of graphemes for the denotation of the metric values ounce and semuncia were encountered. For their transcription, ℥ (U+2125) and (U+10192) were used. “Barred O” is represented by ∅ (U+2205) and is widely used to transcribe the word instans instead of ꝋ (U+A74B) that, according to MUFI documentation stands for the abbreviation of obi(i)t (Coulson & Babcock, 2020, p. 10).

Last but not least, in addition to roman numerals, often preceded and followed by dots such as “.ii.”, Arabic numerals are also comprised in the dataset, mainly due to the medical treatises (see Figures 3 and 4).

Production pipeline The data was built using eScriptorium and Kraken for both segmentation of zones and lines (specifically the BLLA model). Manuscripts were annotated successively. First, the manuscript is automatically segmented, then its segmentation is manually corrected, and finally the text is transcribed. Once each sample is entirely annotated, its use of characters is controlled via the ChocoMufin software, while its conformity to the segmentation classification vocabulary is controlled by HTRVX. Finally, data are released on Github.¹⁶ All the combining and abbreviation signs suggested for use by the present adaptation of Pinche’s guidelines can be also found on a custom-made eScriptorium keyboard configuration, in order to facilitate reuse and compatibility with the guidelines.¹⁷