Making Old Kurdish Publications Processable by Augmenting Available
  Optical Character Recognition Engines
Blnd Yaseen Hossein Hassani
Kurdish libraries have many historical publications that were printed back in
the early days when printing devices were brought to Kurdistan. Having a good
Optical Character Recognition (OCR) to help process these publications and
contribute to the Kurdish languages resources which is crucial as Kurdish is
considered a low-resource language. Current OCR systems are unable to extract
text from historical documents as they have many issues, including being
damaged, very fragile, having many marks left on them, and often written in
non-standard fonts and more. This is a massive obstacle in processing these
documents as currently processing them requires manual typing which is very
time-consuming. In this study, we adopt an open-source OCR framework by Google,
Tesseract version 5.0, that has been used to extract text for various
languages. Currently, there is no public dataset, and we developed our own by
collecting historical documents from Zheen Center for Documentation and
Research, which were printed before 1950 and resulted in a dataset of 1233
images of lines with transcription of each. Then we used the Arabic model as
our base model and trained the model using the dataset. We used different
methods to evaluate our model, Tesseracts built-in evaluator lstmeval indicated
a Character Error Rate (CER) of 0.755%. Additionally, Ocreval demonstrated an
average character accuracy of 84.02%. Finally, we developed a web application
to provide an easy- to-use interface for end-users, allowing them to interact
with the model by inputting an image of a page and extracting the text. Having
an extensive dataset is crucial to develop OCR systems with reasonable
accuracy, as currently, no public datasets are available for historical Kurdish
documents; this posed a significant challenge in our work. Additionally, the
unaligned spaces between characters and words proved another challenge with our
work.