# Transcription Guidelines for Generalized Automatic Text Recognition

Benjamin Kiessling

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# Transcription Guidelines for Generalized Automatic Text Recognition

Benjamin Kiessling

December 22, 2025

## Abstract

The digitization of historical text has advanced significantly in recent years, yet progress remains markedly uneven. While Automatic Text Recognition (ATR) has emerged as a crucial tool, driving successful large-scale digitization campaigns for medieval Latin-script collections, coverage of historical material produced by many of the world's other major manuscript traditions remains fragmented. We postulate that a primary prerequisite for producing generalized text recognition models in a specific domain is the adoption of *universal* transcription guidelines. These guidelines are essential for enabling the distributed annotation of interoperable training data at an inter-project level. Formulating such guidelines is a complex task that requires the reconciliation of scholarly, technological, and economic dimensions. By outlining the principal considerations of each dimension and demonstrating their implementation in a case study, we aim to lay the groundwork for upscaling historical ATR.

## 1 Introduction

The digitization wave of the past two decades has significantly increased online access to historical machine-printed and handwritten works. Despite this progress, a substantial number of these documents are available only as images, lacking machine-readable text. Automatic Text Recognition (ATR) has emerged as a vital tool for converting these images into text, facilitating the analysis of vast historical collections. Nevertheless, advances have been relatively uneven. On the one hand, projects such as CATMuS[17] have produced and integrated large scale, multilingual, and diachronic datasets in order to train freely-available generalized text recognition models for open source software. On the other hand, comparable initiatives are lacking for most of the world's major textual traditions, despite the existence of considerable communities of ATR practitioners and pressing research needs for digital corpora.

It would be a mistake to point towards deficiencies in the technologies used in our ATR systems to explain this state of affairs. Although ATR on Latin-script material can be considered to be the most mature, not least because of the outsized interest in the recognition of modern Latin-script texts, language- and script-specific heuristics have gradually disappeared over the last decades. There are no fundamental technical reasons that explain why there are text recognition models that achieve acceptable character error rates on any cleanly written medieval Latin manuscript, while comparable models for Arabic, Hebrew, or Greek are utterly absent.

The thesis of this article is that an essential requirement for the development of the size of datasets required to produce such generalized ATR models are transcription guidelines that are sufficiently flexible that they can be used for a wide range of Humanities research questions, are economical to implement, and are adapted towards the capabilities of current ATR technology. Only when all of these requirements are fulfilled simultaneously can there be that virtuous cycle of iterative refinement that ends up with truly generalized text recognition models.

## 2 ATR in the Historical Sciences

The deployment of ATR in the historical sciences has matured significantly over the past decade, evolving from experimental prototypes into a core component of digital research infrastructure. Despite this progress, the field remains characterized by a stark imbalance between the sophistication of its recognition technologies and the methodological underdevelopment of its data creation practices.

### 2.1 The State of Transcription Guidelines

While the production of ground truth data is universally acknowledged as the prerequisite for training effective models, the conceptual framework governing this production, transcription guidelines, has largely been
neglected. Historically, ATR models and the majority of datasets prepared for training were constructed without explicit, documented transcription norms. While comprehensible for many modern handwriting datasets, such as IAM [14] or RIMES, that were created by asking scribes to write a predefined digital text, even prominent datasets, e.g., [21, 20, 5], used regularly as benchmarks in the historical document analysis community lack any description of the guiding principles of transcription whatsoever, leaving users to reverse-engineer the annotation logic from the data itself.

Where guidelines do exist, they frequently suffer from significant limitations. Some are overly vague or underspecified, as seen in the REPUBLIC dataset [24] of 17th-century Dutch handwriting. Its documentation, a single paragraph in total, begins with the claim that “The basic transcription rule we followed was simple: as literal as possible”, but immediately caveats that “the documents contain characters for which there is no equivalent on modern keyboard...we used characters that were most similar, or used one character for several characters in the text that seemed to have one specific meaning, such as "et cetera"”[24, dataset paper, p. 3]. Crucially, there is no specification of what these characters are, no character inventory, and not even a note on the keyboard layout used, preventing even a motivated reader from systematically estimating the likely substitutions.

Research projects with significant involvement from humanities scholars often define comprehensive trans-
scription guidelines, but these are frequently not formulated with the ambition to support broader application.
Some, such as [3], which adapts the DTA guidelines [8] but deviates from them in critical ways, conform entirely
to the idiosyncratic requirements of a single project. Others, such as RASAM [28], are fundamentally suitable
for general use, yet no effort has been made to dissociate the guidelines from the originating research project, ef-
fectively blocking their continued development and independent refinement. Although initiatives like CATMuS
have attempted to formulate guidelines suitable for wider, multi-project domains, they remain the absolute ex-
ception in a landscape dominated by ad-hoc, project-specific conventions. This lack of standardization not only
prevents the aggregation of disparate datasets into the large-scale corpora necessary for training generalized
models, but also imposes high start-up costs for new initiatives, as acquired ATR expertise is effectively siloed
rather than shared with the wider community.

## 2.2 The State of Recognition Technology

In contrast to the fragmentation of transcription practices, the technological landscape of ATR has achieved a remarkable degree of convergence. The dominant paradigm for historical text recognition has settled, for the time being, on pipelines consisting of a layout analysis step that identifies text lines and regions, followed by a text recognizer that transcribes the entire line without further explicit segmentation. While a variety of data models (such as bounding boxes and baselines) and detection approaches (ranging from hand-built heuristics to object detectors and semantic segmentation) are in widespread use for layout analysis, text recognition has defaulted to recurrent neural networks (RNNs) trained with Connectionist Temporal Classification loss [7]. Numerous systems following this basic configuration with only minor variation have been proposed over the years, [22, 4, 15] being representative. These have proven robust enough to handle the vast majority of linear writing systems, meaning that for most historical scripts, the fundamental problem of character recognition is, if not entirely solved, feasibly solvable in principle. While significant challenges persist, particularly regarding the segmentation of complex or fragmentary documents, or the recognition of highly variable scripts, the core task of transcribing lines of text is no longer a blocking technical hurdle.

Parallel to this algorithmic convergence, a similar consolidation has occurred in the software stacks and platforms employed by the community. Powerful recognition engines like Calamari[29], kraken[11], and Tesseract[25], alongside user-friendly platforms like eScriptorium[26] and Transkribus[10], have successfully brought historical text recognition within reach of humanities scholars lacking deep technical expertise.

Looking forward, the field is witnessing a slow but perceptible shift towards more powerful methods built on transformer architectures. Models such as [2] promise superior generalization capabilities, particularly when trained on sufficiently massive datasets, and sometimes offer relaxed modelling requirements, for instance by dispensing with segmentation altogether [6]. However, their adoption in the historical domain is currently constrained by multiple factors: they are often initialized with pre-trained large language models [12] that do not exist for many historical language states; they frequently fail to converge on the small datasets common in historical research; and they are prone to hallucinations [1, 9], where the model generates plausible text that is entirely unrelated to the page content, a risk that is significantly lower in the more constrained RNN approaches.

3 The Role of Transcription Guidelines in ATR

Transcription guidelines are a codified framework of principles and conventions that determine how the textual
content of a source document is to be represented in a transcription. They define the level of granularity and
fidelity to the original document, specify the treatment of orthographic variation, abbreviations, punctuation, and uncertain readings, and establish procedures for handling illegible or damaged text. By articulating these parameters, transcription guidelines ensure internal coherence within a project and, if adopted communally, comparability across related corpora. Crucially, they also link the transcription to the specific research questions and analytical objectives of a given project, determining which textual features are considered significant for study and which may be normalized or omitted. In doing so, transcription guidelines make explicit both the methodological and interpretive stance adopted toward the source material.

Within conventional humanities research, transcription guidelines underpin the preparation of scholarly edi-
tions, corpora, and other forms of textual scholarship. They function as an essential instrument for mediating
between the material characteristics of the source and the analytical or interpretive purposes of the edition. Edito-
rial decisions about the representation of orthography, punctuation, and layout are thus codified in the guidelines,
shaping both the interpretive framing of the text and its subsequent reuse in research. In this traditional context,
transcription guidelines primarily address the representation of linguistic and textual features; their domain is
largely confined to the textual layer of the document rather than its visual or structural composition.

In the context of ATR, particularly for historical documents, the concept of transcription guidelines acquires a broader and more technically oriented scope. Beyond describing how textual content should be transcribed for the creation of ground truth data, such guidelines frequently also define how the spatial and structural components of a document are to be annotated. This includes the segmentation of pages into regions into lines, as well as the delineation of relationships among these elements, for example how marginalia or interlinear additions are to be annotated. The articulation of these procedures is critical for ensuring the consistency and interoperability of training data used in ATR systems, where precise alignment between image regions and textual transcriptions underpins model performance. In this sense, ATR transcription guidelines extend the traditional editorial function into a computational domain, bridging philological precision with the requirements of machine learning and document image analysis.

While the importance of transcription guidelines is starting to be recognized by ATR practitioners, the degree of formalization varies substantially between traditional scholarly editing and the historical ATR community. In the former, transcription handbooks have a long history and taxonomies, such as the Canterbury Tales Project's distinction between graphic, graphetic, graphemic, and regularized transcription[19], provide a structured vocabulary for articulating different levels of textual abstraction and editorial intervention. By contrast, within the field of historical ATR, comparable theoretical frameworks are still largely absent. Although ATR projects produce more or less detailed operational guidelines for segmentation and ground-truth transcription with some regularity, there has been comparatively little research on the conceptual or typological foundations of these practices. As a result, the field lacks a shared terminology for distinguishing between levels of representation and their implications for model training and evaluation, as significant methodological heterogeneity persists.

Addressing this theoretical deficit requires a fundamental shift in the perspective on transcription guidelines.
They can no longer be conceived merely as the personal working instruments of a scholar dedicated to the ex-
haustive study of a single object. Instead, they must be reimagined as shared infrastructure capable of scaling
across diverse and often heterogeneous collections. To achieve this, guidelines must move beyond implicit as-
sumptions suited to a single context and rely on explicit, broad conventions across all dimensions, scholarly,
technical, and structural. Ultimately, they must be consciously constructed not as a bespoke lens for a specific
inquiry, but as a reusable artifact intended for general application.

4 Guidelines as a Balancing Act

The construction of transcription guidelines in historical ATR entails a complex balancing act across multiple and often competing axes of requirement. These encompass, among others, the need to retain information relevant to the humanities research questions driving a project, the practical considerations of annotation and data management, and the technical constraints inherent to machine learning models. Ideally, transcription guidelines operate as a negotiated interface between the interpretive priorities of humanities scholarship and the operational requirements of computer vision and natural language processing. In practice, however, this balancing process remains largely informal and intuitive, typically undertaken by practitioners who have acquired sufficient interdisciplinary experience to navigate the challenges posed by all stakeholder domains. The effectiveness of this process typically relies more on accumulated tacit knowledge within research teams than on explicit methodological frameworks.

As this experience is usually skewed toward one disciplinary orientation, the resulting guidelines are rarely free from asymmetries. Participants within ATR projects frequently possess uneven levels of expertise in areas such as paleography, historical linguistics, or computational modeling. This gradient of expertise can produce guidelines that are insufficiently balanced, reflecting, for instance, the predominance of technical efficiency over
the preservation of features significant to humanities research, or conversely, an overemphasis on textual or vi-
sual detail that complicates computational processing. A common manifestation of this imbalance in computer
science-led projects is the underspecification of how particular script elements should be transcribed due to a
lack of paleographical expertise, leading to inconsistent treatment of ambiguous letterforms, ligatures, or abbre-
viations. Such issues often arise from limited cross-disciplinary communication and a lack of shared awareness
of the problem space that the guidelines must address. Consequently, the resulting transcription handbooks,
while operationally adequate, may embody implicit compromises that constrain both the scholarly utility of the
transcriptions and the robustness or generalizability of the resulting ATR models. These same shortcomings
also tend to undermine the reusability of both the guidelines themselves and the datasets produced with them,
as their methodological premises and conventions are often insufficiently explicit or inconsistently applied to
support reliable reuse beyond the original project context.

## 4.1 Humanities Research Diversity

The first and perhaps most determining dimension in the construction of transcription guidelines is the specific humanities research program that the resulting data is intended to serve. Historical documents are objects that operate on multiple layers simultaneously: they are physical artifacts bearing visual marks, linguistic records preserving specific orthographies and morphologies, and carriers of semantic meaning. Different research disciplines prioritize these layers differently, creating a tension between the desire to capture the material specificity of the source and the need to access its informational content. This tension manifests as a continuous spectrum of transcription styles, ranging from hyper-diplomatic to heavily regularized or normalized (Fig. 1).

At one end of this spectrum lies the paleographer, whose research questions often depend on the precise recording of graphetic distinctions. For a study on the evolution of script styles, the distinction between allographs, the precise rendering of ligatures, and the preservation of idiosyncratic punctuation are not merely ornamental details but the core data of the inquiry. Transcription guidelines tailored to these needs are necessarily hyper-diplomatic, instructing transcribers, human and machine alike, to reproduce the visual evidence of the page with high fidelity. The resulting ground truth is highly faithful to the image but yields a digital text that is less than amenable to search or process using standard NLP tools without significant post-processing.

At the opposite end of the spectrum are historians, literary scholars interested in distant reading, or social scientists utilizing quantitative methods. For these researchers, the primary value of the document lies in its semantic content, identifying named entities, tracking thematic trends, or performing topic modelling. Variations in spelling, archaic abbreviations, or complex layout are mere noise that obscures the signal. Consequently, transcription guidelines driven by these objectives often favour regularization or normalization. Here, abbreviations are silently expanded, whitespace and punctuation is modernized to aid segmentation, and archaic spellings may be standardized to maximize searchability.

The methodological choice of where to land on this spectrum is fundamentally tied to the strategic stage of the ATR implementation. A critical decision in guideline construction is whether to task human annotators with normalization, creating a normalized dataset, or to insist on a diplomatic transcription, leaving the normalization to be accomplished later through NLP post-processing. Training an ATR model on normalized text requires the model itself to internalize the normalization rules, whereas training on diplomatic datasets maintains a direct, strict alignment between the visual input and the textual output. This second approach allows the downstream user to apply programmatic NLP tools to convert the diplomatic ATR output into a normalized format suitable for distant reading, thereby maximizing the overall versatility of the dataset.

Transliteration, the systematic conversion of characters from a source script to a target script, acts as a form of hyper-regularization and further complicates the transcription landscape. A relatively common practice in fields dealing with non-Latin scripts that are historically difficult to reproduce faithfully in printed form, such as those used to write Ottoman Turkish (Arabic), Mycenean Greek (Linear B), or Sanskrit (various Indic scripts), it presents a unique point of friction between community norms and interoperability when the source script is also used to write other languages. An extreme example is the case of Ottoman Turkish. Its scholarly community, unlike modern Arabic and Persian studies, largely operates with texts transliterated into Latin script. Datasets produced to community conventions are therefore radically different from those produced for co-scriptal languages, rendering them almost entirely incompatible.

## 4.2 Versatility

The second critical dimension in the construction of transcription guidelines is versatility. In the context of producing ground truth for ATR, versatility is defined as the capability of the guidelines to be effectively applied to material outside the immediate confines of the originating project. This aspect is imperative because it directly dictates the potential for data aggregation. The development of robust, generalized ATR models, models capable
in allem vnferm Trübsal/das wir auch können
trösten/die da sind in allerley Trübsal mit dem
Trost/damit wir von GOTT getröstet wer-
den/ Hochgelobet vnd geliebet in Ewigkeit/

<table><caption>Figure 1: An example of three different transcription norms on the graphetic-normalized spectrum for an early modern German print. The major editorial interventions are underlined.</caption><thead><tr><td>Editorial choices</td><td>Transcription</td></tr></thead><tbody><tr><td>Retaining ligatures, distinction between long (f) and round (s), no normalization of u and v</td><td>in allem vnferm Trübsal/ das wir auch können trösten/ die da sind in allerley Trübsal mit dem Trost/damit wir von GOTT getröstet werden/ Hochgelobet vnd geliebet in Ewigkeit/</td></tr><tr><td>Unification of s-forms, umlauts, and hyphenation, dissolution of ligatures</td><td>in allem vnserm Trübsal/ das wir auch können trösten/ die da sind in allerley Trübsal mit dem Trost/damit wir von GOTT getröstet werden/ Hochgelobet vnd geliebet in Ewigkeit/</td></tr><tr><td>Complete modernization of spelling, capitalisation, and punctuation, dissolution of ligatures.</td><td>In allem unserem Trübsal, das wir auch können trösten, die da sind in allerlei Trübsal mit dem Trost, damit wir von Gott getröstet werden. Hochgelobet und geliebet in Ewigkeit.</td></tr></tbody></table>

of performing well across diverse hands, layouts, and time periods, relies on the accumulation of large and diverse training datasets. These datasets are rarely produced by a single project. Rather, they are formed by aggregating corpora from multiple sources. The challenge of versatility is therefore a challenge of interoperability: not only if it is possible to transpose the guidelines from the originating project to new corpora but also if it is possible and convenient to do so while remaining compatible with the research requirements of other parts of the scholarly community.

**Applicability within the Writing System** On a fundamental level versatility arises from the treatment of the writing system itself. Historical scripts are not fixed in time and place but are profoundly diachronic and diatopic. A versatile guideline effectively captures the core graphemes of the script without becoming overfitted to the idiosyncracies of a specific document, place, or time. Narrowly constructed paleographic norms, for instance around a single manuscript, are prone to capturing the allographic noise instead of the signal of the script class. Guidelines rigorously classifying specific scribal features such as ligatures or unique punctuation marks from a certain period are unlikely to be applicable to a corpus of texts written two centuries later where those features have disappeared or evolved. The problem space here is principally one of granularity: the more precisely a norm models the visual idiosyncracies of a specific place and era, the less transferable it becomes to other documents sharing the same script. This creates a tension between research needs and the generic representation required for dataset compatibility.

**Linguistic Independence** Many major scripts serve as the substrate for numerous languages, often appearing side-by-side within the same historical collections. This problem is compounded by the fact that the boundaries between historical languages and dialects are frequently fluid, making the confident delineation of one language from another difficult. This fluidity, combined with the common presence of mixed-language texts, complicates any guideline set that relies on language-specific rules, underscoring the critical necessity for multi-lingual text recognition models in historical ATR. Hence, versatility requires that guidelines remain functional when the underlying language of the text changes.

However, transcription rules are frequently, and often implicitly, entangled with specific linguistic assumptions. Guidelines may rely heavily on language-specific phonological rules (e.g., "only transcribe this mark if it modifies pronunciation") and will fail when applied to a language where that mark serves a different or purely
ornamental function. The challenge, therefore, is whether the guidelines can maintain consistency when the lin-
guistic rules governing the text shift, or whether they force the creation of separate, non-interoperable datasets
for every language variant.

**Heterogeneity of Scholarly Traditions** Finally, versatility is constrained by the heterogeneity of local scholarly traditions. Even when projects share the same script and language, they may adhere to divergent editorial norms which are frequently shaped by historical precedent rather than strictly enforced methodological requirements. While the choices represented by these localized norms can be arbitrary, guidelines that fail to respect these historically arbitrary conventions risk rejection by parts of the scholarly community, regardless of the technical quality of proposed norms. The friction here is sociological: guidelines that align perfectly with one scholarly community often alienate others, making it difficult to create a standard that is widely acceptable across multiple domains.

**4.3 Transcription Efficiency**

Transcription efficiency needs to be considered from three different angles: the initial time needed to train a human transcriber of a given level of expertise to a desired standard, the output they can achieve once trained, and the complexity of ensuring quality of the output of the transcription process. This dimension represents the economic constraint on data annotation, balancing philological detail with the volume of training data needed for robust ATR models.

**Training Time and Skill Floors** A critical factor in transcription efficiency is both the time required to teach a new transcriber and the minimum amount of prior expertise and exposure to similar material needed to apply the guidelines with a desired level of accuracy. Guidelines that are paleographically granular, i.e., require differentiation between a large number of allographs, or linguistically complex, i.e., require normalization based on historical rules, significantly raise both. Even relatively simple guidelines often demand at least a passing familiarity with the material in question before training even begins, while the most intricate guidelines can only be implemented consistently by expertly trained paleographers. Complex transcription norms therefore do not only drastically increase the time-to-competency but shrink the recruitment pool to those possessing a higher level of initial training.

**Transcriber Throughput** Once transcribers are trained, the primary metric becomes the rate at which they can annotate usable training data. Although throughput is influenced by external factors such as available tooling, script complexity, and the conservation state of the material, it remains inversely correlated with the complexity of the guidelines. Applying intricate rules requires frequent interruptions where transcribers must deliberate on categorizations, thereby significantly reducing the production rate compared to simplified transcription approaches. Further, paleographically complex guidelines typically build on large character inventories that not only force transcribers to make these laborious distinctions, but also interrupt the input flow to access shortcuts or character pickers.

**Inter-Annotator Agreement and Quality Assurance** The complexity, consistency, and completeness of the guidelines, in addition to the initial training, directly influence how efficiently the quality of the final corpus can be assured. Underspecified guidelines will lead to low inter-annotator agreement, the rate at which two independent transcribers produce the same output for the same document image, as transcribers will make judgement calls in order to compensate for a lack of guidance from the pre-defined transcription rules. On the other hand, comprehensive but complex guidelines with numerous rules and exceptions also invariably reduce inter-annotator agreement, as it becomes impossible for transcribers to keep the entirety of the transcription ruleset in working memory. Either case necessitates intensive quality assurance workflows, often relying on senior scholars to arbitrate disagreements or perform full secondary reviews, introducing a bottleneck in the annotation process. Conversely, guidelines that prioritize simplicity and unambiguity tend to yield higher intrinsic consistency, reducing the need for expensive post-transcription correction.

**4.4 Technological Adaptedness**

The final dimension of the balancing act is technological adaptedness. While transcription guidelines function as instructions for human annotators, the ultimate consumer of the resulting data is a machine learning algorithm. Current ATR systems, whether based on recurrent neural networks or transformer architectures, operate under
specific constraints regarding how they map visual inputs to textual outputs. Guidelines that ignore these in-
ductive biases and limitations of the technology, regardless of their philological precision, will produce a corpus
that is difficult for the model to learn, leading to inferior performance.

**Visual-Textual Alignment and Determinism in Normalization**
The fundamental mechanism of ATR is the correlation of pixel regions to character labels. The model "reads" by learning that a specific visual feature corresponds to one or more labels that, in turn, make up specific character code points. Guidelines that mandate extensive normalization or the expansion of abbreviations disrupt this correlation if the normalization rules are not sufficiently deterministic, particularly given the limited context within which most text recognition methods operate.

Human readers rely on a wide variety of contextual hints to understand written text and disambiguate difficult
passages, drawing on information from adjacent lines, the broader document structure, or external metadata
regarding the expected language and vocabulary. In contrast, most current text recognizers operate strictly at
the line level, making predictions solely based on the statistical model formed during training and the visual
evidence of a single line image.

When guidelines require transformations of the visual input into a textual sequence contingent on informa-
tion not obtainable from the line image, that is, when the transformation is not a deterministic conversion of a
small group of signs into a fixed text, recognition accuracy will suffer. Such transformations take many forms:
the regularization of spacing, punctuation, and orthography; complex abbreviation resolution; semantic disam-
biguation of signs; and the addition of vocalization to unvocalized abjads. Not all of these pose equal difficulties.
For instance, a specific abbreviation sign that consistently resolves to the same string is likely to be learnable by
most methods. However, complex abbreviation resolutions, such as those involving a generic suspension mark,
e.g., a trailing period, which maps to multiple different resolved strings depending on semantic context, are likely
to be fragile.

A consideration that merits specific attention here is the role of implicit language modelling within the recog-
nition method. All text recognition models, even simple character classifiers, form an internal model of the
languages on which they are trained. While generally weak compared to dedicated language models, as their
linguistic context is limited to the granularity of the input data (signs, lines, or pages), this internal modelling is
still significant. For example, applying a model trained on French material to Dutch documents in the same scribal
style will often result in output that hallucinates French orthography. Recognition models with a higher capacity
for internal language modelling are inherently more capable of handling ambiguity in transcription guidelines,
as they can systematize better from the training data to form accurate hypotheses about likely language use.

There is, furthermore, a critical overlap with the versatility aspect when discussing determinism. It is entirely
possible that a transcription norm is deterministic enough for accurate recognition in the context of a single
project, or even multiple projects in isolation, but will cease to be technologically adapted when these datasets
are aggregated. An illustrative example is found in abbreviations. A guideline mandating the resolution of
abbreviations is likely to be compatible with technological limitations when applied to a particular, thematically
restricted corpus containing only a limited set of expansions. However, when such datasets are aggregated to
train a generalized model, the diversity of possible resolutions for identical visual signs often becomes too high
to be accurately modelled using current text recognition methods.

**Corpus Size and Class Distribution** Guidelines must account for the statistical requirements of all machine
learning methods: models need a sufficient number of examples to learn a class effectively. In turn this means
that the corpus size needed to successfully train a text recognition model is not only determined by the inherent
complexity of the written material to be processed but also by the output class vocabulary of the transcription
norm.

Historical writing, particularly when analyzed through a paleographic lens, frequently exhibits a long tail of rare characters. Guidelines that enforce a hyper-diplomatic approach, assigning unique labels to every possible allograph, inevitably fragment the dataset. In such scenarios, rare signs may appear only a handful of times, resulting in recognition models that fail to generalize these signs as distinct classes. To achieve acceptable accuracy on these under-represented classes, the required volume of training data increases disproportionately. Conversely, merging allographs into parent graphemic classes increases the statistical "support" for each label, thereby reducing the error rate for a fixed amount of data.

A similar dynamic governs guidelines that mandate a high degree of normalization. When a model is required
to normalize text, for instance, by expanding abbreviations or standardizing orthography, it must internalize not
merely the visual identity of characters but also the context-dependent rules governing their transformation.
To learn these often non-deterministic mappings, the model requires a significantly larger number of examples
to discern the underlying logic. Complex normalization rules thus impose a higher threshold for the minimum number of annotated lines necessary to train a competent model.

Recent qualitative experiments conducted by the Swedish National Archives provide empirical support for this relationship between guideline complexity and data efficiency.<fnref n="1" /> These experiments involved generalized Medieval Latin-script models [23] developed for the retro-digitization of Latin manuscript collections. Among others, the study compared two models trained using the kraken ATR package: one following the semi-diplomatic TRIDIS guidelines [27], and the other adhering to the predominantly graphemic CATMuS guidelines [16]. While both guidelines and models are well-suited to the 15th-century manuscripts evaluated and both mandate some degree of normalization, notably word spacing, they diverge in that TRIDIS requires the resolution of abbreviations.

The evaluation concluded that both models performed equally well on the target material, suggesting that the choice between them is largely a matter of editorial preference for the degree of normalization in the output text. However, this parity in performance is particularly striking given the disparity in training data volume: the TRIDIS model relied on a corpus of approximately 560,000, predominantly Latin, lines, whereas the CATMuS model was trained on only 160,000 lines with only circa 20,000 in Latin, a nearly thirty-fold difference in language-specific data. Although neither model has reached a saturation point where additional data yields no improvement, the comparison strongly suggests that streamlined, graphemic guidelines allow for the training of performant generalized models with significantly less ground truth data.

<figure><img src="image_3.png" /><figcaption>Figure 2: The conceptual relationship, *ceteris paribus*, between model accuracy, corpus size, and guideline adaptedness. Complex guidelines impose larger training data requirements than adapted guidelines for the same error rates.</figcaption></figure>

**Layout Analysis** We have largely skipped the question of segmentation, as there are limited degrees of freedom when modeling layout, at least for text lines, that do not render the data annotated by different groups non-interoperable. The prevalent annotation platforms, eScriptorium and Transkribus, and se-

rialization standards, PageXML [18] and ALTO [13], but also other software packages in historical ATR such as tesseract, HTRFlow, or Pero OCR effectively restrict the geometric data model to a strict hierarchy of regions and text lines encoded as either baselines or bounding boxes. While a major balancing act here lies in defining region and line typologies, this is only of minor importance to the actual training of text recognition models, which, after all, are only trained on line images and their associated transcriptions.

Nevertheless, the choice of geometric data model is critical for determining the usability of the annotated data for ATR. Simple bounding box models, while computationally inexpensive and conceptually straightforward, are poorly suited to the rotated, skewed, or highly curved lines frequently found in historical manuscripts. In such cases, bounding boxes inevitably capture substantial amounts of pixel data from neighbouring lines or background artefacts, introducing noise that the recognizer must learn to ignore. Baseline models, by contrast, are versatile enough to represent arbitrary curvature and orientation, allowing the extraction of line images that closely follow the actual text flow and minimize contamination from adjacent content. This improves the effective signal-to-noise ratio of the input and makes the resulting corpora substantially more amenable to training robust models.

Technological adaptedness in layout analysis also encompasses the handling of annotation depth, particularly in the presence of complex textual interventions such as interlinear corrections or marginal notes. From a philological perspective, there are often good reasons to integrate such additions directly into the logical text flow in order to approximate the intended reading order. In the absence of adequate support for this kind of structural annotation on the textual level, it is tempting to implement this integration on the segmentation level through means such as spatially joining interlinear elements to the baseline or polygon of the main line, thereby creating a single, semantically coherent unit (Fig. 3). Technically, however, this practice is usually counterproductive: most line recognizers are optimized for inputs with relatively consistent vertical extent and smooth,

<footnote n="1">¹Meaningful quantitative evaluation of models trained to different transcription norms is laborious as it requires retranscription of the test set for each model; hence, it is rarely undertaken.</footnote>
approximately linear progression, and they tend to perform poorly on lines with pronounced vertical excursions or highly irregular baselines.

<figure><img src="image_4.png" /><figcaption>Figure 3: Different treatment of interlinear corrections in an Early New High German tax registry (StAAG U.17/0004). The corrections of the second line from the top are annotated separately, the bottom line and its correction is treated as a single unit. The latter can generally not be processed by current text recognition methods.</figcaption></figure>

Recent recognition methods based on high-capacity transformer-style neural networks can, to some extent, accommodate images that contain noise from adjacent lines, vertical intrusions, or erratic baselines. Yet corpora designed specifically for such models face substantial drawbacks, including markedly higher training data requirements, significantly increased computational costs, and a greater tendency toward over-normalization and hallucination in the output. For the purposes of humanities research, where resources are finite and the exception is often of particular scholarly interest, these conglomerated representations should only be chosen for the most high resource material.

# 5 Principles around "Good" Transcription Guidelines

Drawing upon the practical experiences of the historical ATR community, it is possible to postulate a set of principles for constructing "universal" transcription guidelines.

In this context, "universal" does not imply a single standard applicable to every project, script, or language without modification, but rather a design philosophy that renders the resulting data fit for the broadest possible utility. Such guidelines are designed to be suitable for transcribing material across wide chronological periods and diverse script variants, they are optimized for speed and learnability by non-expert annotators, and they are adapted to the limitations of current machine learning architectures. The ultimate objective of these principles is to be able to produce aggregated datasets that serve as a foundation of robust generalized models, while remaining rich enough to support, potentially via postprocessing, a wide gamut of humanities research questions.

## 5.1 The Graphemic Compromise

The foundational principle of universal guidelines is the graphemic transcription as a starting point. Reducing each letter form to its grapheme while preserving the fidelity to the visual features of the text on the document page ensures that the class inventory remains relatively manageable, can be transcribed without specialized tooling, and that the transformations applied during the annotation by human transcribers are mechanistic enough to be learnable by current machine learning technology. Thus, this approach occupies a sweet spot between the extremes of hyper-diplomatic transcription, difficult to standardize in a handbook and slow to annotate but easy to learn with sufficient data, and normalized transcription, easy to standardize but slow to annotate and increasing the risk of hallucination.

## 5.2 Regularization and Omission for Human Reliability

A key insight from practical experience is that certain textual features, while to be retained in a purely graphemic transcription and potentially of interest to scholarly investigation, are either prone to subjective judgement or impose a disproportionate cognitive load on human transcribers, leading to inconsistent transcriptions and high quality assurance efforts. Universal guidelines will opt to regularize or omit these cases, in order to optimize the amount of transcriptions that can be obtained with a certain amount of resources.
**Regularization of Whitespace** Consistent word separation with whitespace is a distinctly modern phenomenon in most languages and the irregularity of spacing in many handwritten texts makes the decision on whether the space between to characters represents just a minor deviation from the normal inter-character distance or is truly a whitespace highly subjective and prone to inconsistencies from transcriber to transcriber. While introducing word segmentation by normalizing whitespace is a fairly intrusive linguistic intervention, it is rightfully considered to be a difficult problem that frequently relies upon language-specific algorithms, it can be a necessity in order to obtain sufficient inter-annotator agreement. It is therefore preferable to normalize whitespace to modern conventions when word separation is applied inconsistently in the target domain.

**Omission of Facultative Vocalization** Abjads, such as Arabic or Hebrew, often feature facultative vocalization, i.e., scribes will place diacritics marking vowels only in cases where disambiguation is deemed necessary. Proficient readers of these languages typically supply vocalization naturally during reading, making the partial, inconsistent markings redundant for comprehension. Furthermore, the scribe's choice of where to place vocalization is often subjective, reflecting local tradition or individual preference rather than a strict linguistic requirement. Accurately transcribing these sporadic, subjectively placed marks is prone to error even for specialists. Attempts to capture partial vocalization often results in datasets riddled with noise that is difficult to correct for. Consequently, it is far more efficient to instruct transcribers to omit vocalization entirely. These omissions present no problem to current text recognition methods as they will happily learn to ignore superfluous diacritics.

## 5.3 Deterministic Disambiguation

Universal guidelines should prioritize rules that different annotators and machines can apply identically without deep semantic knowledge. A guiding principle should be the limited-context test: A human transcriber should be able to transcribe a line correctly using the same amount of context the machine learning model has access to, i.e., in general they should produce an accurate transcription when presented with the image of an individual text line image.

## 5.4 Composability

To ensure the creation of truly reusable data, universal guidelines must prioritize composability: the capacity of independent datasets to be aggregated into a single training corpus without introducing conflicting ground truth definitions or degrading adaptedness. As the performance of generalized models relies heavily on the volume and diversity of training data, guidelines must be designed to minimize friction when merging corpora from different projects, languages, or time periods. This requires limiting interpretive interventions to those that are mechanistically applicable across boundaries.

**Script Fidelity over Linguistic Interpretation** The central tenet of composable guidelines is again script fidelity; the imperative is to transcribe the writing system, not the underlying language. Historical scripts often serve as the substrate for numerous languages; for example, the Arabic script has historically been used for Arabic, Persian, Ottoman Turkish, Urdu, and Malay, among others. If transcription guidelines are entangled with language-specific phonological rules or orthographic conventions, the resulting data becomes siloed. A model trained on a corpus where a specific glyph is transcribed according to linguistic norms in one language may fail when exposed to the same glyph used in another.

To achieve composability, transcription must decouple the visual recognition task from the linguistic interpretation task. A text written in the Arabic script should be transcribed the same way, regardless of whether the language is Ottoman Turkish or Persian. This approach ensures that datasets from distinct linguistic domains sharing a common script can be aggregated to train robust "script-level" models. These generalized models can then leverage the shared visual features of the script across languages, effectively using Persian data to improve the recognition of Ottoman manuscripts, and vice versa.

**The Pitfalls of Transliteration** The threat to composability is most acute in the practice of transliteration. While common in philological traditions for a number of languages, transliteration introduces a layer of abstraction that is frequently non-deterministic and heavily dependent on semantic context.

When ground truth is transliterated, the text recognition model is forced to implicitly learn not only the visual shapes of the characters but also the complex conversion rules between the source and target scripts. In a multi-lingual context, this requires the model to "code-switch" between different transliteration norms based on the language it detects, a task that dramatically increases the difficulty of the learning process. By rejecting transliteration as a task to be performed by ATR and strictly adhering to the original script, guidelines ensure
that the data remains technically adapted to the capabilities of current ATR architectures while maintaining the flexibility to be joined with other corpora.

## 5.5 Evaluation of Technological Adaptedness

Technological adaptedness exists on a spectrum: well-adapted guidelines facilitate the training of accurate, generalized text recognition models using relatively small corpora, whereas poorly adapted guidelines necessitate significantly larger datasets to achieve comparable error rates. While the correlation between guideline complexity, corpus size, and model accuracy is well-established, quantifying "adaptedness" into a precise metric remains impossible. The tremendous variability inherent in historical data, ranging from the conservation state of the material and the consistency of scribal hands to the specific inductive biases of different ATR architectures, renders a universal formula for data estimation largely theoretical.

In ideal scenarios, characterized by well-preserved material, highly standardized hands, and alphabetic scripts with limited graphetic variation, a graphemically transcribed dataset of merely a few thousand lines may be sufficient to consistently achieve character error rates below 5% using standard RNN line recognizers. However, cases where these optimal criteria converge are bound to be the rare exception, particularly when the objective is to train models that generalize beyond high-prestige scribal styles. Furthermore, for many non-Latin writing systems, the lack of extensive paleographic frameworks means the true variability of scribal features is often unknown at the outset. This uncertainty renders a hypothetical formula to compute an adaptedness score from the mentioned variables useless.

Consequently, the settled practice in the field is pragmatic rather than predictive: researchers must rely on iterative experimentation. The standard workflow involves selecting an ATR architecture, training a pilot model with a corpus size estimated from prior experience, and evaluating whether the resulting accuracy meets the project's research requirements. If the evaluation yields negative results, the project faces a binary choice: either increase the training corpus to force the model to learn the current guidelines or investigate ways to simplify them to improve learnability.

In this context, the choice of model architecture plays a critical role in the validation of guidelines. While recent high-capacity transformer-based systems, e.g., TrOCR, offer superior performance on massive datasets, they are ill-suited for the initial phase of guideline construction. These systems typically require substantial volumes of data to converge and produce a minimally working model, imposing a high up-front annotation cost before the soundness of the transcription rules can even be tested. In contrast, lightweight conventional recognizers are highly advantageous for early evaluation. Because they converge rapidly, often with only a few hundred lines of ground truth, they function as a reliable early indicator. If a lightweight model fails to learn a specific transcription rule from a small sample, it strongly suggests that the rule is either inconsistently applied by annotators or insufficiently deterministic for machine learning. Failing fast allows researchers to detect and correct flaws in their guidelines early in the project lifecycle, preventing the costly waste of resources involved in producing thousands of lines of ground truth based on defective transcription principles.

## 5.6 Development, Documentation, and Machine-Actionability

The traditional mode of scholarly publication, static, finalized guidelines attached to a project's terminal output, is fundamentally ill-suited to the needs of generalized ATR. If transcription guidelines are to serve as the foundation for interoperable, community-wide datasets, they must be conceptualized not as fixed artifacts but as living documents. Just as software evolves to address new edge cases and feature requests, transcription guidelines must possess the elasticity to expand and refine themselves as they are applied to new manuscripts, scribal hands, and time periods.

To ensure this longevity and fitness for purpose, the maintenance of guidelines should transition from the proprietary domain of individual research projects to a community of practitioners. Experience from established collaborative standards bodies, such as the Text Encoding Initiative or the ALTO XML board, demonstrates that broad adoption relies on a sense of communal ownership. When guidelines are perceived as the idiosyncratic rules of a single project, external scholars are incentivized to create competing standards rather than contribute to existing ones. Conversely, when governance is transparent and participatory, the guidelines can function as a shared infrastructure, aggregating the collective expertise of paleographers, historians, and computer scientists.

This shift in governance necessitates the adoption of technical best practices from open-source software development. First and foremost, transcription handbooks and their associated character inventories should be available in digital form, appropriately licensed, and rigorously versioned. The latter communicates essential information to the user: a change in the major version implies a breaking change in transcription logic, e.g., a new normalization rule that renders previous data incompatible without conversion), while minor updates indicate backwards-compatible refinements or clarifications.
Further, guideline projects should actively adopt the infrastructure of collaborative software development. Deliberation and decision-making should occur on public channels such as mailing lists and chat rooms, the documentation itself should be hosted in version control repositories on platforms like GitHub or GitLab, rather than static files created and distributed through opaque channels. This infrastructure empowers community members to directly contribute, for instance, by reporting ambiguities or proposing refinements of the norms. Such a workflow not only democratizes the maintenance process but also creates a transparent historical record of decision-making.

Finally, the guidelines should be, to the greatest extent possible, machine-actionable; that is, their imple-
mentation should be verifiable using automated tooling. While it is impossible to algorithmically verify the
philological correctness of a transcription without pre-existing ground truth, it is entirely feasible to validate
structural compliance. Basic verifications, such as adherence to the authorized character set, can be automated
provided that machine-readable inventories are published as supplements to the human-readable handbook.

6 Designing Guidelines for Reusable and Generalizable Datasets: The CATMuS Example

The CATMuS guidelines exemplify the shift toward conceptualizing transcription as a shared computational art-
tifact. Rather than targeting editorial adequacy for a specific scholarly readership, these guidelines prioritize sys-
temic coherence and scalability to support collective data production and model training. To this end, CATMuS
adopts a strict graphemic approach, reducing allographic variation to standardized representations and privileg-
ing visual similarity over fine-grained distinctions, in order to minimize annotator subjectivity. This strategy
is a deliberate methodological compromise: it acknowledges that the full complexity of a manuscript cannot be
captured in a generic ATR dataset without introducing levels of subjectivity that undermine data homogeneity
and ultimately model performance.

This principle of reduction for the sake of consistency is applied across three key dimensions.

**Alphabetic Variation** Distinctions between *u/v* and *i/j* are often anachronistic for medieval texts and applied inconsistently by scribes. To prevent the interpretative drift that occurs when transcribers attempt to apply modern linguistic norms, CATMuS enforces a reduction to single standardized representations, e.g., *u* for both *u/v*.

**Punctuation** The diverse inventory of medieval punctuation, such as the *punctus elevatus* or *punctus versus*, is rarely governed by a stable system transferable across corpora. Consequently, to ensure consistency these are simplified to a minimal set of signs (e.g., simple and double points) based on visual form rather than rhetorical function.

**Abbreviation Signs** In the absence of a computational consensus on a computationally tractable and pale-
ographically meaningful abbreviation encoding, CATMuS relies on visual and functional equivalence. Distinct
marks that cannot be robustly and consistently identified by annotators, for instance macrons and tildes, are
treated as as allographs and simplified to a single base form.

By structuring guidelines around a small number of systemic principles rather than exhaustive rules, CATMuS
reduces cognitive load for annotators, a critical factor in large-scale collaborative projects. Crucially, this generic
baseline is designed to maximize interoperability without precluding specialization. Models trained on CATMuS-
compliant data provide a robust foundation that can be fine-tuned on smaller, curated datasets to recover specific
features, such as complex abbreviation systems or script-specific allographs. In this framework, transcription
becomes a cumulative, adaptive process within a scientific commons, where generic interoperability supports,
rather than constrains, specialized inquiry.

7 Conclusion

The ongoing transition from bespoke, project-specific ATR models to generalized, multi-purpose engines repre-
sents a fundamental paradigm shift in the digital humanities. As we have argued, the primary obstacle to this
transition is no longer strictly technological: the neural architectures available today are sufficiently powerful to
handle the complexities of historical script, provided they are fed with data of sufficient volume and consistency.
The true bottleneck lies in the fragmentation of the corpus landscape, where the lack of shared standards renders
the vast majority of existing datasets mutually incompatible.
Overcoming this impasse requires the community to embrace a new conception of transcription, one that views ground truth not as the terminal output of a specific philological inquiry, but as a reusable artifact for the field at large. The outlined principles of versatility, transcription efficiency, and technological adaptedness offer a blueprint for this transformation. With the proposed "graphemic compromise", we can construct datasets that are economically viable to produce, composable across project boundaries, and prioritize learnability by current text recognition methods.

Fundamentally, this shift depends not only on methodological adjustments but also a change on how the community governs its scholarly outputs. Guidelines must cease to be proprietary artifacts and instead become living standards that are maintained collaboratively by a community of practice.

The standardization of ground truth production is a strategic imperative and the only viable pathway to creating the virtuous cycle of model training and iterative refinement necessary to close the digital divide between the modern and well-resourced and everything else. By building this shared foundation, we move closer to a future where high-quality automatic text recognition is not a luxury for a few select domains, but a universal utility for the historical sciences.

# 8 Acknowledgements

I would like to express my gratitude to Ariane Pinche for her valuable suggestions on improving this article. I also thank her for generously providing the foundation used in the CATMuS example section.

# 9 Funding

This publication has received funding from the European Union's Horizon research and innovation programme under grant agreement No. 101132163 (ATRIUM) and the BACK IN TIME project.

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