Transkribus: Historical Documents with AI

A Complete Guide to Features, Limitations, and Real-World Applications

by Elif Sevval Akdeniz

Transkribus has transformed how researchers, archivists, and historians approach the transcription of handwritten historical documents. As with any technical tool, Transkribus has both remarkable potential and great limitations that users should know about before jumping in.

In this in-depth guide, I will cover practical use cases where the platform excels and areas where it falls short. Finally, I will offset the cost-effectiveness and performance trade-off to help you determine if Transkribus is a worthwhile investment for your specific research needs and budgetary constraints.

What is Transkribus?

Transkribus is an artificial intelligence (AI) platform to transcribe and analyse historical texts, specializing in Handwritten Text Recognition (HTR) and Optical Character Recognition (OCR). Developed in the READ project (Recognition and Enrichment of Archival Documents), Transkribus addresses one of the most time-consuming issues of historical research: transforming hand-written manuscripts into searchable text.

Unlike common transcription software used on modern audio or speech, Transkribus is particularly designed to work with historical documents, manuscripts, and archival content. The site provides services and tools for the digitization, transcription, recognition, and search of historical documents, which are critical for researchers working with centuries-old texts, medieval manuscripts, or any handwritten historical documents.

The Strengths: What Transkribus Does Exceptionally Well

Advanced Handwritten Text Recognition

Transkribus excels at recognizing various handwriting styles, ranging from medieval scripts to 19th-century script. Transkribus' AI models are trained on vast collections of historical documents, enabling them to decipher handwriting patterns that would be difficult even for experienced paleographers. It works particularly well with steady handwriting and well-preserved manuscripts.

Multilingual and Multi-Script Support

I explored Transkribus's potential not only for modern European languages but also for historical scripts. For instance, its support for Gothic Latin manuscripts, Ottoman Turkish records, and 19th-century Dutch cursive demonstrates the tool’s capacity to handle multilingual, multi-script archival content aligning with our work on Oorlogsdagboeken and highlighting its value in Digital Text Analysis contexts.

The same goes for various writing systems, from Latin scripts to Gothic fonts, and even non-Latin alphabets in some cases.

This graph shows the institutional READ-COOP members' (European cooperative society running the Transkribus platform) research production by their number of publications. This co-op model involves more than 90 institutional members—including archives, universities, and libraries—who collectively guide the development of the platform. Additionally, Transkribus is currently managed and developed by the READ-COOP SCE.

Collaborative Features and Document Management

Transkribus has robust collaboration capabilities, allowing parallel work of research teams on transcription tasks. Versioning, roles of users, and project management are some of the platform's features support large digitization projects with multiple contributors.

Integration and Publishing Options

Transkribus Sites offers an academic web space to publish and share electronic versions of transcribed historical texts. It offers enhanced search facilities, metadata functionalities, and personalized view modes, enabling researchers to create open-access materials from their HTR output. For example, users can publish handwritten manuscripts and their transcripts side by side, enabling the broader academic community to navigate, search, and cite the documents within digital humanities research.

The Limitations: Where Transkribus Faces Challenges

Accuracy Varies Significantly with Document Quality

Transkribus's precision relies heavily on the source document’s condition and quality. Faded ink, poor scans, water marks, or faded parchment can significantly impact levels of recognition. While AI gets along superbly with good, well-preserved documents, researchers working with challenging material may still spend hours doing manual corrections.

Inconsistent Performance Across Different Script Types

Although Transkribus is capable of processing a wide range of historical scripts, its performance varies greatly depending on the complexity of the script and the amount of training data. For instance, 18th- and 19th-century English or German cursives which are well-represented yield very good results. Medieval manuscripts with paleographic phenomena like abbreviations, ligatures, or context-dependent glyphs, however, typically must be corrected manually post-processing since the accuracy is lower, even with models trained individually.

Context and Semantic Understanding

Like most OCR and HTR software, Transkribus is character-based, with a focus on visual pattern recognition rather than semantic sense. This means it copies what it "reads" but lacks the ability to understand linguistic context. As noted by Springmann et al. (2020), “A character-level model may identify shapes accurately, but without contextual information, it cannot reliably disambiguate abbreviations or reconstruct damaged portions of a manuscript. Semantic-aware systems address this by integrating corpus-trained language models.” This distinction is critical for historical text processing, where understanding the semantic context such as distinguishing domus (house) from dominus (lord) is often necessary for accurate transcription.

Conjugate, in contrast, semantic OCR/HTR systems attempt to introduce linguistic, grammatical, or historical context into the recognition process. These systems, typically still experimental, make use of language models, probabilistic inference, or context-sensitive transformers to guess based on meaning (Nockels et al., 2022, p. 373; Schomaker, 2020, p. 223). As an example, Kraken+ contextual decoding configuration or more recent research by Springmann et al. (2020) combines HTR with language modelling to improve transcription of early modern texts with uncertain characters.

Credit System and Pricing: Understanding the Financial Framework

Free Plan Limitations

When you sign up for Transkribus, you are allocated 50 free credits a month for text recognition, which allows the users to experiment with the platform and create small projects. The recent reports suggest that the free version offers just 100 free credits for transcriptions a month with a 5MB upload limit per image.

The credit system is based on pages: for handwritten material it uses 1 credit per page, and for printed material 0.5 credits per page. Therefore, free accounts can manage between 50-100 pages of material per month, varying based on the nature of the document.

Paid Subscription Structure

Subscription options include 300 pages annually for €19.90/year, while 500 pages costs €59/year. The pricing structure shows significant cost increases for higher volumes, which can be challenging for researchers with extensive document collections.

For organizational users, there's an automatic 50% reduction in credits consumed per page when using the Metagrapho API, making it more cost-effective for institutions with large-scale digitization projects.

Scholarship Opportunities

Transkribus offers scholarships that provide free credits for handwritten text recognition technology, granted based on specific thesis or research projects. Some universities such as the Radboud University provide up to 1000 free credits for students and employees, demonstrating institutional support for digital humanities research.

Use Cases: When to Choose Transkribus

Ideal Applications

Transkribus is ideal in several scenarios: large-scale digitization programs where hand transcription would be extremely time-consuming, research studies with a focus on specific scribes or consistent handwriting styles, collaboration in transcription involving numerous researchers, and projects requiring searchable digital repositories of documents from history.

When to Consider Alternatives

 

Transkribus might not be the most suitable option for highly damaged or low-quality documents where manual palaeographic expertise is essential, for projects with tight budgets that cannot afford the platform’s credit system, for scripts or languages poorly supported by current models, or in cases where extremely high accuracy is required and no human post-correction is possible. In such scenarios, relying solely on automatic transcription could result in critical errors, making human intervention indispensable.

Additionally, while Transkribus supports basic tagging and structure annotation, more thematic coding flexibility, hierarchical categorization, and qualitative querying are possible within applications like NVivo, ATLAS.ti, or ELAN. Therefore, in projects that go beyond transcription and embrace qualitative pattern analysis, discourse mapping, or participant annotation, it is most beneficial to export the text (for example, in XML or TXT) and transfer the analysis to NVivo or similar systems.