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International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 3/2023

24.05.2023 | Special Issue Paper

Printed Ottoman text recognition using synthetic data and data augmentation

verfasst von: Esma F. Bilgin Tasdemir

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 3/2023

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Abstract

The Ottoman script, which was in use for over five centuries, is an Arabic alphabet-based writing system. It became obsolete after the change of alphabet in Turkey. There are plenty of Ottoman documents, overwhelmingly printed in Naskh style. This work presents a DL-based character recognition system for the printed Ottoman script. We first generate a synthetic text image dataset from a text corpus and then augment it using some image processing methods. We develop a hybrid convolutional neural network-bidirectional long short-term memory recognizer and train it with the original and the augmented datasets. Finally, we apply a transfer learning procedure for adapting the system to real image data. The proposed system obtains 0.11 CER on synthetic data and 0.16 CER on real data comprising of line images from a printed historical Ottoman book.
Vorheriger Artikel Large-scale genealogical information extraction from handwritten Quebec parish records
Nächster Artikel Online handwriting trajectory reconstruction from kinematic sensors using temporal convolutional network

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Fußnoten
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Metadaten
Titel
Printed Ottoman text recognition using synthetic data and data augmentation
verfasst von
Esma F. Bilgin Tasdemir
Publikationsdatum
24.05.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 3/2023
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-023-00436-9

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