Handling Big Manuscript Data

Elpida Perdiki and Maria Konstantinidou

This contribution describes a series of Handwritten Text Recognition (HTR) tests performed on medieval Greek manuscripts and run on Transkribus, a platform for collaboratively transcribing and retrieving historical documents. [1] Transkribus was chosen on the basis of its suitability for our research, its user-friendly interface, and its accessibility. [2] We mainly used the HTR component of the platform.

Our research involves various works by John Chrysostom. They are distributed in ca. 3000 manuscripts, each surviving in at least two recensions, one being a revision of the other. We seek to classify the manuscripts according to the version they transmit. Regardless of the classification method that will be used (String Matching, Neural Language Model, or other, non-traditional methods), [3] a digital transcription is required. Due to the size of the task, we use automated transcription, HTR. For classification purposes, only a minimum of 80% accuracy is required. Even a Character Error Rate (CER) of more than 20% might be adequate, since much of the erroneous characters involve spaces, [4] accents and breathings, [5] which can be easily eliminated in subsequent analysis.

Obtaining large training data sets for such texts is not straightforward given the scarcity of digital or digitized editions and the way information is presented (in negative or mixed type apparatus). Furthermore, matching these extracted, decontextualised data to the respective manuscript images requires a high level of human interference. [6] One way of creating training data is by using HTR-generated transcriptions, smoothed manually, [7] although it is not advised by Transkribus, which recommends accurate transcriptions as ground truth. [8] Other methods include crowdsourcing (requiring additional resources) and data augmentation. [9]

Manually producing (at least partial) transcriptions is, therefore, inevitable. In an effort to limit manual input, we experimented on: (a) training data size reduction up to 93%, [10] (b) transcribing new manuscripts using existing models trained entirely on other manuscripts, [11] and (c) training of mixed models using very small samples from multiple manuscripts.

Transkribus does provide for multiple scribes in a single archive but recommends larger training sets in such cases. [12] In addition to the multiple hands involved, our material lacks the uniformity often found among documents of a single archive. On the other hand, our research benefits from transcribing the same work many times, and from a relative script uniformity. [13]

The performance of the models trained is exhibited in the accompanying poster. Irrespective of the training set size, we applied the following settings to all models:Our results proved that Transkribus is a cost-effective tool for the transcription of medieval Greek manuscripts written in standard book-hands, which is the case for the vast majority of Chrysostom’s manuscripts. The transcription of hundreds of codices would require considerable human resources and/or highly specialized technical expertise. These are usually available only in larger-scale funded projects, with individual researchers and smaller project teams precluded from such research. The method tested in this study proposes a feasible way of compiling enough training data and of substantially automating the process of transcribing large numbers of manuscripts. That could have a major impact on the textual studies of popular authors, such as Chrysostom.

Understanding the phenomenon of the two recensions in (some of) the works of one of the most popular authors in the history of the Greek-speaking world will considerably enhance our knowledge of Byzantine scholarship, editorial activity, and manuscript production, to name but a few; it will also substantially affect modern editorial attitudes and decisions for scholars of Chrysostom. For the examination of this phenomenon, we currently rely on a few articles on in-progress work that was never completed, as well as on long-outdated editions. Time and again, attempts to examine the issue were impeded by the volume of the texts and the number of the manuscripts involved. [17] Thus, for lack of relevant literature, one must turn to the primary sources afresh. For centuries, the painstaking task of transcribing such a volume of manuscripts deterred any aspiring scholars and editors. The latest technological advances in HTR and tools like Transkribus have rendered this once impossible task perfectly realistic.