Neural OCR Post-Hoc Correction of Historical Corpora

Neural OCR Post-Hoc Correction of Historical Corpora Lijun Lyu1, Maria Koutraki1, Martin Krickl2, Besnik Fetahu1,3 1L3S Research Center, Leibniz University of Hannover / Hannover, Germany 2Austrian National Library / Vienna, Austria 3Amazon / Seattle, WA, USA lyu@L3S.de, koutraki@L3S.de, martin.krickl@onb.ac.at, besnikf@amazon.com Abstract Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic vari- ations, typefaces, or language evolution (i.e., new letters, word spellings), as the main source of character, word, or word segmen- tation transcription errors. For digital corpora of historical prints, the errors are further exac- erbated due to low scan quality and lack of language standardization. For the task of OCR post-hoc correction, we propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. At character level we flexibly capture errors, and decode the corrected output based on a novel attention mechanism. Accounting for the input and out- put similarity, we propose a new loss function that rewards the model’s correcting behavior. Evaluation on a historical book corpus in German language shows that our models are robust in capturing diverse OCR transcription errors and reduce the word error rate of 32.3% by more than 89%. 1 Introduction OCR is at the forefront of digitization projects for cultural heritage preservation. The main task is to identify characters from their visual form into their textual representation. Scan quality, book layout, visual character sim- ilarity are some of the factors that impact the output quality of OCR systems. This problem is severe for historical corpora, which is the case in this work. We deal with historical books in Ger- man language from the 16th–18th century, where characters are added or removed (e.g., long s – ), word spellings change (e.g., ‘‘vnd’’ vs. ‘‘und’’) that often lead to word and character transcription errors. Figure 1 shows examples pages conveying the complexity of this task. There are several strategies to correct OCR transcription errors. Post-hoc correction is the most common setup (Dong and Smith, 2018; Xu and Smith, 2017). The input is an OCR tran- scribed text, and the output is its corrected version according to the error-free ground-truth transcrip- tion. For instance, Dong and Smith (2018) use a multi-input attention to leverage redundancy among textual snippets for correction. Alterna- tively, domain specific OCR engines can be trained (Reul et al., 2018a), by using manually aligned line image segments and line text (Reul et al., 2018b). However, manually acquiring such ground-truth is highly expensive, and further- more, typically, historical corpora do not contain redundant information. Moreover, each book has its own characteristics—typeface styles, regional and publisher’s use of language, and so forth. In this work, we propose a post-hoc approach to correct OCR transcription errors, and apply it to a historical collection of books in German language. As input we have only the OCR transcription of book from their scans, for which we output the corrected text, that we assess with respect to the …

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