Challenging Error Correction in Recognised Byzantine G reek (2024)

Abstract: Automatic correction of errors in Handwritten Text Recognition (HTR) output poses persistent challenges yet to be fully resolved. In this study, we introduce a shared task aimed at addressing this challenge, which attracted 271 submissions, yielding only a handful of promising approaches. This paper presents the datasets, the most effective methods, and an experimental analysis in error-correcting HTRed manuscripts and papyri in Byzantine Greek, the language that followed Classical and preceded Modern Greek. By using recognised and transcribed data from seven centuries, the two best-performing methods are compared, one based on a neural encoder-decoder architecture and the other based on engineered linguistic rules. We show that the recognition error rate can be reduced by both, up to 2.5 points at the level of characters and up to 15 at the level of words, while also elucidating their respective strengths and weaknesses.

## introduction the digitisation of ancient texts plays a crucial role in both analysing ancient corpora and preserving cultural heritage. however, transcribing ancient handwritten text using optical character and text recognition methods remains a challenging task. handwritten text recognition (htr) concerns the conversion of scanned images of handwritten text into machine-readable text. in contrast to recently printed materials, the analysis of images containing handwritten documents presents more intricate difficulties, particularly when dealing with historical and premodern manuscripts. these challenges may result in recognised text containing numerous errors or, at times, a complete inability to recognise the text. this is especially true when there is a low availability of suitable training data for specific scripts, such as medieval scripts. ## related work most studies approach the task of post-correction by focusing on printed text and by employing 2 to distinguish between the two, we will refer to 'transcribed' when the text is generated by a human expert and to 'recognised' when it is generated by a system. ## the shared task we used a dataset ( §3.2) to set up a shared task on error-correcting the htr output of byzantine papyri and manuscripts. the challenge lasted from may 1st to july 1st, 2022, counting one hundred thirty-six registered participants from around the world, foot_1 and 271 submissions. ## methods for our error correction task, which aims to push the system transcription closer to the respective human transcription, we opted for three baselines ( §4.1), which were shared with the participants of the challenge. upon the evaluation of all the submissions, using the system and the synthetic transcriptions as input, we investigate further the two best-performing submitted approaches: one based on predefined rules and the other utilising a text-to-text transformer. foot_2 ## discussion the challenge in error-correcting the htr output of byzantine manuscripts and papyri has attracted a significant number of registrations and submissions ( §3.2.3), the best of which were discussed in this work. characteristics of byzantine greek and the respective scripts have been discussed in §3.1, in order to highlight the difficulties that recognition and error-correction algorithms need to tackle. the variety of scripts and scribes in this language, along with its evolution, is likely to have caused a varying recognition error rate over time (figure 3 ). this error rate variety poses a significant challenge to post-correction methods, which should be able to handle lines that comprise from few to many errors (different types). when assessing error correction in recognized printed and handwritten material, it's crucial to consider the error rate. as detailed in §2, prior studies have predominantly focused on printed material, characterised by relatively low recognition error rates. however, our findings illustrate a significant variation in the error rate for htr output, encompassing both accurate recognitions and those with numerous errors (fig. 4 ). we also show that a rule-based approach outperforms the baselines ( the experimental results presented in ## conclusions we presented a challenge of error-correcting htr output for byzantine greek, publicly releasing data with both synthetic and actual htr errors. a pretrained byt5 encoder-decoder model, fine-tuned on recognised (input; encoded) and transcribed (output; decoded) texts, achieves a notably high performance, effectively reducing errors. a comparable reduction of errors could have been achieved if the htr model had been trained on approximately 30 additional pages. however, generalisation remains a concern, as evidenced by the model's performance on synthetic data, where errors were introduced instead of corrected. a rule-based approach, on the other hand, showed promise by well performing on synthetic data but not on real-world data. future work will focus on challenging errorcorrection systems based on htr models trained on data from specific centuries, aiming to address the diverse range of errors encountered.