The 17th c. is crucial for the French language, as it sees the creation of a strict orthographic norm that largely persists to this day. Despite its significance, the history of spelling systems remains however an overlooked area in French linguistics for two reasons. On the one hand, spelling is made up of micro-changes which requires a quantitative approach, and on the other hand, no corpus is available due to the interventions of editors in almost all the texts already available. In this paper, we therefore propose a new corpus allowing such a study, as well as the extraction and analysis tools necessary for our research. By comparing the text extracted with OCR and a version automatically aligned with contemporary French spelling, we extract the variant zones, we categorise these variants, and we observe their frequency to study the (ortho)graphic change during the 17th century.
One of the main technical challenges for carrying out such a study relies on the existence
of important amounts of data, in order to guarantee quantitatively the reliability of the results.
Unfortunately, such corpora of classical French do not exist for two reasons. On the one hand,
as Cl. Vachon 5[5, p. 32, n. 31] bitterly experienced, text editors got into the habit of
standardising the language of that 2e2r,a16[], which makes its study particularly complicated, if
not impossible to use for graphematic studies. On the other hand, the few corpora that have
been created, like that of Cl. Vachon, but also others like thaRtésoefauthCeorpus Français
Préclassique et Classique (RCFC) [
This paper proposes to return to the history of the Fvrêetnemchent graphique (“graphic clothing”) in a computational way. We introduce a two-step approach: first, a unique corpus creation pipeline meticulously extracts spelling information from digital facsimiles. This pipeline includes a layout analysis model to distinguish text from paratext on the page, an OCR model that retains the historical charſa›c,tpeivro‹tal to written French, and a linguistic normaliser that “translates” historical French into its contemporary counterpart at the sentence level. In the second step, we analyse the created corpus using a comparison algorithm that matches the extracted historical text with its modern equivalent at the character level. This enables us to pinpoint significant variations, categorise these diferences, and uncover detailed trends throughout the 17th century. This methodological framework not only enhances our understanding of historical French orthography, but also proposes a new approach for computational linguistic studies of spelling variation.
Corpus building from OCR has long been a task in digital humanities and corpus
linguistics. Initially deemed unsuitable for historical sources i4n4]1,9O93C[R gained credibility in
the late 1990s for corpus building, including XML TEI formalisation in commercial projects
such as thePatrologia Latina Database, and for Ancient Greek scripts in the 201409s].[Most
project using TEI, such as the First1KGreek project, relied on manual formalisation of the text’s
logical structur4e0][, as manual work was considered essential for accuracy. The advent of
user-friendly OCR and HTR technologies has spurred interest in automatic document
formalisation (ADF), primarily focused on facsimile formalisa5t2io]nan[d noisy text removal with
tools based on vocabularies such as SegmOn2t5o], [which standardises the identification of
paratextual zones (running titles, footnotes, etc.). Few projects, however, have utilised font,
geometric, and textual features to reconstruct or emulate the original text structure from
borndigital PDFs or OCR outputPsa.perXML [
Linguistic Normalisation (LN) has a long history, dating back to the 8107’]s, [but has
developed itself as derived task from Machine Translation (MT) in the beginning of the 2010’s,
usually to improve downstream tasks in the pipeline such as linguistic ann5o4t].atLiNon [
share important similarities with MT, and therefore relies on the same methods, but with a
slightly diferent objective: to “translate” a source into another state of the language, usually
more recent (16th c. Germa→n contemporary German), rather than into another language
(Italian→ German). Resources existed first for Slovene, German, English, Hungarian, Spanish,
Swedish, Portuguese8[], but several studies have recently improved both resou21r]ceasn[d
techniques for historical French, first comparing rule-based, statistical and neural23m]e,thods [
and then alignment-based and neural MT-approa6c]h.es [
Computational scriptology is based on the notion socfripta, coined by Remacle4[
3.1. Data For practical reasons, a first corpus of limited size (c. 600 texts) spanning the 17th c. was produced with our pipeline. The data comes from Gthalelica digital library and contains only French-language documents. For our experiment, we have selected only plays, which ofer medium size documents (compared to novels, potentially much longer), and linguistically homogeneous data (spelling can influenced by the genre, such as legal documents which tend to use more “archaic” traits and may involve Latin phrases).
Our pipeline allows us to extract data, enrich it and store it in a standard for1m).at (cf. fig. Firstly, we apply a layout analysis model specialised in theatrical data trained for the occasion, then we use an OCR model prepared for this study which preserves thse (l‹oſ›n).gBased on the layout analysis we convert ALTO files to TEI files. Only textual data which contains text of the work (paragraph, speech, verse, etc.), and not linked to the structure of the book (running title, page number, quire marks, etc.) is extracted and normalised automatically, before to be reintroduced into the TEI file.
Layout analysis. Based on the resultsTable 1
of Najem-Meyer and Romanello41[] and Training and evaluation data for Layout
Analthe initial evaluation of YOLO region sryeesgsise-ntacroass sitnhgele dadtaosceutms.ent Einach thiemagdeatarseept-.
menter against Kraken’3s6[] as a region
segmenter with YALTAi1[
DigitizationArtefactZone 189 23 24 tised version of our corpus, which couldQuireMarksZone 183 29 15 include empty pages (e.g., bookbinding,MainZone-Head 159 28 33 cover) and full pages. This resultedDMraoipnCZaopniet-aSlZp-oCnoentinued 113564 2136 1283 in a corpus of 620 images for trainingG,raphicZone-Decoration 130 16 22 evaluation, and testing. Our final corMM-aaiinnZZoonnee--LEgntry 5889 65 119 pus comprises 32 null pages (without anM-ainZone-P 41 10 12 notations) and a variety of annotatioMMnaasir,ngZinoTneex-tPZ-oCnoen-MtinauneudscriptAddendum 2218 31 72 with a majority of speech-related tagMsarginTextZone-Notes 30 1 0 (pMaariantZeoxnteu:aSPl-,rMealiantZeodneo:bSjPe#cCtosnt(Neiu.ngmu.b,eedr)-, SSMttaaamminppZZZooonnne-eeO-Stthicekrer 112394 331 522 ingZone, RunningTitleZone), a smaller TitlePageZone 8 2 4 number of logical structuring featurGMersaaipnhZiocnZeo-nInecipit 54 01 10 such as scene titleMsa(inZone:Head) and MainZone-Signature 1 0 0 cast listsMa(inZone:Entry), as well as a few paragraphs and poetic excerpts, mainly found in incipits or prefaces of the books (MainZone:P), as seen in tab1.. (a) Groundtruth.
YOLO is well integrated within YALTATir,aining and evaluation data for OCR.
which in turn works seamlessly with
Kraken, we decided to use the latter tDoataset Century Language Books Lines
train a new OCR model that includes thTerain/Dev 16 French 7 17817
longs (‹ſ›). Kraken, unlike other OCR sys- Train/Dev 17 French 19 20267
tem, avoids the integration of a strong laTnra-in/Dev 16 Latin 12 10648
guage model which in turn, for our purTT-eesstt 1176 FFrreenncchh 1100
pose, allows for keeping more variationsT.est 18 French 10
This new model, derived from CATMuS
Print 2[
Linguistic normalisation. All documents are processed via a normaliser previously traine3d. Only text contained inp><and <sp> (“speech”) elements are kept for normalisation, because a specific spelling variation occurring in the running title, for instance, would be repeated every two pages and potentially alter artificially the result of the scriptometric analysis. The text is split into sentences (ending by a full stop, an exclamation or a question mark) or subsentences (ending by a colon or a semicolon), all storesdeign>a(“<arbitrary segment”) element, with the source text oinri<g> (“original form”) and the automatically normalised text in r<eg> (“regularization”). The normalised version is evaluated against a dictionary of modern French to control the quality of the final product.
Layout analysis. We evaluate two possible setups: both use fine-tuning with the original YOLOv8L models and an input image size of 960 pixels (higher than the default). One setup uses only the dataset produced in the context of this paper, while the other merges this dataset with the larger LADaS dataset (5,000 images). We train both setups for 100 epochs with otherwise default parameters.
Since our study focuses exclusively on MtahienZone, which contains the primary text and excludes all paratextual elements (such as decorations, page numbers, and running titles), we have concentrated our evaluation on this specific zone. Overall, when considering all classes, we found that integrating our data with the LADaS corpus yields improved results (0.768 vs. 0.8). However, for the most critical clasSspesa(ndSp-continued), the model trained exclusively with theatrical data produces slightly better outcomes. As previously mentioned, these are the classes essential for our study. 3https://huggingface.co/rbawden/modern_french_normalis.ation Text recognition. To fine-tune and Table 4 adapt the CATMuS Print OCR model tCoharacter and word error rates for both models. the allographic variation of rosu/lnodng Models Characters Errors CER WER s, we modified the classifier codec (-- No fine-tuning 38394 924 2.41 11.06 resize new mode) and used a standard Fine-tuning 38394 649 1.69 8.34 learning rate of 0.0001, along with a batch size of 32. This logical approach ensures
Table 5 the model is fine tuned to the specific ty-Character and word error rates for both models. pographic variations without relying on any learned behaviour during the formal%i-errors CER (part) Errors Correct Generated sation process. We compare this approach 8.78% 0.14% 57 SPACE to a model without fine-tuning, trained 7.55% 0.13% 49 ' ’ from scratch, with the same architecture 6.62% 0.11% 43 s ſ (cf. tab.4), revealing the superiority of the 23..2737%% 00..0055%% 2118 ſ– fØ approach with fine tuning. 2.62% 0.04% 17 ’ '
Most of the errors are errors related to2.16% 0.04% 14 Ø SPACE poor segmentation of the text (cf.5t)a,ibn. 22%% 00..0033%% 1133 .1 IØ which there should be a space that is miss- 1.85% 0.03% 12 ◌́ Ø ing from the prediction – a classic error for1.69% 0.03% 11 , .
1.54% 0.03% 10 0 o historical prints. The prediction errors re1-.54% 0.03% 10 t r garding two types of apostrophes (curved 1.54% 0.03% 10 ◌̂ Ø or straight) are of little concern because they do not afect the result from a linguistic point of view and are due to poor data preparation that is easily correctable. The confusion between thse arnoduntdhe longs is likely attributable to the fine-tuning process and the absence of thseinlotnhge base model. Linguistic normalisation To evaluate the results of the normalisation, we compare the prediction of the normaliser with a dictionary of contemporary French to obtain a Word Accuracy (WAcc). Results are satisfactory (cf. fig3.), with a median above 90%. Texts with a WAcc under 80% are removed to avoid using unreliable data.
The final dataset is made of around 80,000 pages for 620 documents. While theFigure 3: Word error rate for the corpus. number of unit is uneven over the years (cf. fig. 5a), the accumulated tokens are progressing evenly (c5fb. )fig. .An example of our TEI encoding is presented in fig4.. <sp> <ab> <seg> <orig>SGANARELLE.</orig> <reg>SGANARELLE.</reg> </seg> <seg> <orig>Promettez-moy donc, Seigneur Geronimo, de me parler avec toute ſorte de franchiſe.</orig> <reg>Promettez-moi donc, Seigneur Geronimo, de me parler avec toute sorte de franchise.</reg> </seg> </ab> </sp> <sp> <ab> <seg> <orig>GERONIMO.</orig> <reg>GERONIMO.</reg> </seg> <seg> <orig>Ie vous le promets.</orig> <reg>Je vous le promets.</reg> </seg> </ab> </sp>
(b) Accumulated tokens over the years. Year of (a) Number of bibliographical units per year. Thperinting is used for the date, any document withbin around 1720 represent printed books fromout a precise date are removed from the plot. Towithin the 17th century but with unclear or kime n-s are taken from the original OCR documents, precise printing dates. only from theMainZones.
We use the ABA 4[
Secondly, for each of the aligned word pairs, the original version and the normalised version are aligned at the character level, still using the Needleman-Wunsch algorithm, but using a specific substitution matrix to allow not only identical letters to be aligned, but also letters considered close in (pre)classical French and contemporary French (presence/absence of diacritic, ligatures…). For example, while identical letters benefit from a substitution score of 4, letters difering only in accent or cedilla benefit from a score of 2, asſ›daon‹d ‹s› or ‹s› and ‹ß› for example. Other pairs of letters benefit from a score of 1, such as ‹u› and ‹v›, ‹s› and ‹z› or even ‹n› and ‹m ›. Conversely, a score of -1 is assigned to pairs of distinct letters not subject to such exceptions, as well as to the deletion or insertion of a character.
This execution of the NeedlemanWunsch algorithm to obtaiTnable 6: Prefix similarity matrix for the original and character-level alignment is tniholer-mparleisveiodusvebrsoixononofth‹eApoopſttrime›a.l paTthhe taorrcoawlcsuliantdeictahtee lustrated in the matrix in t6a,b.similarity between two prefixes, one from the word on where each number representsthe firstOrnowt,hitsheopottihmearl frpoamth,thgerewenordin dinicatthees feirqsutalciotyl-, the similarity score of the besruetmdni.ndicates substitution, and blue indicates deletion. pttaaaurhollnpiieeggfixtcnnbaooermmomsrteefteohnnwia‹ttfsArl.oipbgiFmoofnnooxdſmrutt.iencroenaedIxt›ttoaibmibnastbpegneatlptdiewrnww,ee‹techetAoeeinhcpndiohôs‹eAbtdſtb›rtpbeaebhoos›iyexnttreôpA ↓↓↓↓↓A↘-320141 ↓↓↓↓p→↘754638 ↓↓↓o↘→→8791270 ſ↓↓↓→→→768169 t↓↓↘→→→111058213 ↓r↘→→→→11-4716172 e↘→→→→→ 2-36112161 and ‹Apô›, we must consider the best alignment between ‹Apo› and ‹Apô› (which has a score of 10) then make an inserTt,ion of which has a score of -1, which provides a total score of 9. If we had preferred to first consider the best alignment between ‹Aſ›paond ‹Ap›, which has a score of 6, then deleteôt,hwehich has a score of -1, we would have obtained an alignment with a score of 5, therefore lower than optimal. In case of insertion or deletion during this alignment step, we use the ¤ character in order to obtain two words of the same length in both the original and normalised version. Thus, at the end of this second alignment step, the AwpoorTdtre in the original version is matched withapô¤tre in a normalised version to obtain character-by-character alignment.
Finally, for each word in the corpus, its original and normalised versions are analysed, char4Some subtleties are brought to this adjustment, suetchanads& which are considered equivalent. acter by character, to detect, in the case of diferent characters at the same position, the normalisation rule that applies, or to signal that no existing rule was identified when appropriate. 72 rules were defined based on the bibliography and the diferences observed in the gold FreEMnorm parallel corpu2s1[]. For example, the rulReamist letter is detected if an ‹i›, a ‹j›, an ‹u› or a ‹v› is present in the associated original word respectively to a ‹j›, an ‹i›, a ‹v› or an ‹u› in the normalised version.
Based on the alignments obtained using the Needleman-Wunsch algorithm and the detections of the 72 rules mentioned earlier, our analysis reveals four distinctive patterns of historical spelling changes. The principle underlying this analysis is straightforward: if a normalisation rule is detected less frequently, it indicates that the historical spelling it targets is becoming less prevalent in the corpus. To examine its evolution throughout the century, we normalize the total number of rule applications tFoigitusre 6: Disappearance of ‹gn›. percentage within each text. For instance, the etymological spelling ‹gn›, founcdogi-n form noitre (<lat. cognoscere), is less and less replace by ‹nn› (tocdonanyaître, eng. “to know”), signifying the slow disappearance of this spelling (c6f)..fig.
(a) Substitution of ‹es› by ‹é›.
(b) Substitution of ‹as› by ‹â›.
Pattern A: constant rate. Using ABA, it is possible to detect more complex traits of historical graphic systems than the specific use of a single letter (e.g. ‹u› vs ‹v› as a vowel) or a group of letters (‹gn› vs ‹nn›), such as the presence of a diacritical letter to change the sound-value of the letter to which it is added (e.g. vowel + ‹s›). In historical French, the phoneme [e] is thus regularly noted with the grapheme ‹es› where today we useesta‹ét›v(s état, eng. “state”), and the phoneme [ä] is noted ‹as› where we now find ‹â›pa(sturage vs pâturage, eng “pasture”). If counting the presence of ‹v› followed by consonant (‹vne› = [yn]) to identify the historical use of ‹v› is enough, it is impossible to count the occurrences of ‹es› to measure the presence of a diacriticasl(‹esponge›→ ‹éponge›, eng. “sponge”, but ‹espagno→l›‹espagnol› and not ‹épagnol›, eng. “spanish”): the transition from a complex grapheme (such as a digraph) to a simple grapheme requires an alignment at the character level of the original text and its normalised version, and then the deduction of the spelling change from the diference between the two. (a) Accumulation of occurrences of diferent (b) Theoretical progression of two similar varispellings: two similaers(→é, as→â) and one ants over time, which start at diferent times, diferent (ct→t). Data are scaled to base 100 but progress at the same speed, according to to be comparable. the constant rate hypothesis.
In our corpus, we detect a clear decrease in the use of complex graphemes with a diacritical s, whether the latter is combined with ‹e› (cf.7afig). or with ‹a› (cf. fig. 7b). Interestingly, the propagation of these two new spellings (vowel+accent) does occur at a very similar speed (cf. fig. 8a), recalling Kroch’s constant rate hypothesis (c8bf.)5fig,. of which researchers have already found traces in syntac5t9i]ca[nd phonological18[] change. (a) Apparition of the contemporary use of ramist(b) Disappearance of the etymological combinaletters. tion ‹ct›. 5“When one grammatical option replaces another with which it is in competition across a set of linguistic contexts, the rate of replacement, properly measured, is the same in all of 3t8h]em.” [ Pattern B: abrupt change. On the basis of such observations, it is however possible to go further and date the moment when a break occurs in the scribal practice, to date the moment when the spelling changes. To do so, we can use binary segmentation58(,B4S]), [an algorithm using a forward stepwise method, to identify change-point detection. This method has already been used in diachronic linguistic to study the sudden introduction of new lexi3c7a]l. items [
One of the main discoveries of our study is the extremely abrupt nature of certain changes,
which take place at very high speed, such as the disappearance between 1668 and 1672 of
Ramist letters (cf. fig9.a), as proposed by Christophe Plantin in the 16t1h1c].a[nd defended
by Pierre Corneille in his forewoarudlecteur of 1663 [
(a) Slow decrease of the losn(g‹ſ›).
(b) Increase of the acute accent.
Pasques proposed as one of the parameters for spelling change the type case available: “the typographical use of the ligature has slowed down, in our opinion, the regular replacement of silents by an accent”7[, p. 92]. It is indeed faster to compose the woredſtoit with the ligature (e+ſt+o+i+t=4 characters) than without (e+ſ|s+t+o+i+t=5 characters). One could argue that switching to the accented letter Faiglusore 11: Correlation ‹ſ›/acute accent. requires only four characters (é+t+o+i+t=4 characters), but if the ligatures are present in number in the printer’s type case, the accented characters are less so.
Our working hypothesis is as follows: as ligatures are largely composed osf a(‹ſl›)o,ng we should obtain a correlation between the use osf (tchf.i sfig. 10a) and the acute accent (cf. fig. 10b). We evaluate the correlation between the evolution of the two phenomena over time, and obtain a Pearson product-moment correlation coefÏcient of 0.365 pw-ivtahluae of 4.88e-20, which indicate a good correlation (cf1. 1fig). (a) Increase of the diaeresis.
(b) Increase of the confusion ‹en|m›/‹an|m›.
Pattern D: innovation. Finally, it is important to note that, in this slow movement of standardisation that we are drawing, innovations also appear. These innovations concern a lot diacritics, some of which are exploding in number like the diaeresis (1c0fa.)fig:.scriptors tend to add them more and more on one of the two hiatus vowel, especially with the sequence ‹ue› (louër orloüer, todaylouer, eng. “to rent/to praise”). We also note a great hesitation regarding the notation of nasal vowels (cf.1fig2b.), especially [ã], for which we can use ‹en|m› or ‹an|m› such asaventure vs avanture (todayaventure, eng. “adventure”).
The spelling of the 17th c. is changing throughout the century, and at the beginning of the 18th century the standardisation process is very advanced (etymological letters, use of diacritical letters, historical use of ‹u› and ‹i›, etc.), as studies on other languages, such a31s]Polish [ or English 5[], have been able to demonstrate.We still observe, however, a certain instability, which concerns more minor hesitations than anything else (notation of nasal vowels, hiatus vowel, etc): although the standardisation process is advanced, it is not yet finished.
Among all the changesTable 7
in spelling, all those obM- ain spelling change and their dating using change-point detection.
served seem to follow the Rule Example Change-point
traditional shape of the
scurve, and no “anomalies” ‹‹cctq›u→e›‹→t›‹c› eaxvpecloqiucet→→aexvepcloit 11663328
have yet been found as it tilde → vowel hõme→homme 1637
have been the case else- ‹gn› → ‹nn› incognu→inconnu 1654
where [
The idea of a slow change, which spreads over a long per5i]o,sde[ems to be confirmed by our analyses (cf. tab7.). However, the velocity of change varies greatly from one phenomenon to another, with sometimes slow shifts over decades, or sometimes abrupt ruptures whose cause is not entirely clear, and which would be interesting to discover.
As for the reasons for the change, a lot of work still needs to be done, particularly in trying to find features that could predict the cha3n2g].e O[ne of them, the identity of the printers, would be interesting to evaluate, unfortunately the data is not always available, particularly for the 18th century, which will pose a problem for the future of this study. Nevertheless, some indications suggest that it would be important to review the hypothesis that sees printing as a vector of change5][: the limitations imposed by the type case of printers could for instance be a hindrance to change.
A more precise modelling of these changes is therefore on the agenda for our future research. Whether it concerns the identification of possible reasons for these changes, their more precise dating (in particular by integrating confidence intervals), or the addition of new data for the 18th century. The improvement of all data extraction and enrichment tools has already begun, and should thus allow the creation of an even larger and more precise corpus.
All the data and code is available on our GitHubhrtetppos://github.com/DEFI-COLaF/Theat reLFSV2.
Merci (dans l’ordre alphabétique) à Jean Barré, Alexandre Bartz, Rachel Bawden, Philippe Gambette et Benoît Sagot pour leur aide. À nos relecteur·trices aussi pour leurs excellentes remarques.
This paper has been funded by the DEFI Inria COLCaoFrpus et Outils pour les Langues de France and the FNS-Spark projecNt°220833.
[1] Académie françaiseC.ahiers de remarques sur l’orthographe françoise pour estre examinez par chacun de Messieurs de l’Academie, avec des observations de Bossuet, Pellisson, etc. Charles Joseph Marty-Laveaux. Paris: Jules Gay, 1863. uhrtlt:ps://books.google.ch/boo ks?id=u5Y5AQAAIAAJ.