  Most low-resource languages do not have the necessary resources to create
even a substantial monolingual corpus. These languages may often be found in
government proceedings but mainly in Portable Document Format (PDF) that
contains legacy fonts. Extracting text from these documents to create a
monolingual corpus is challenging due to legacy font usage and printer-friendly
encoding, which are not optimized for text extraction. Therefore, we propose a
simple, automatic, and novel idea that can scale for Tamil, Sinhala, English
languages, and many documents along with parallel corpora. Since Tamil and
Sinhala are Low-Resource Languages, we improved the performance of Tesseract by
employing LSTM-based training on more than 20 legacy fonts to recognize printed
characters in these languages. Especially, our model detects code-mixed text,
numbers, and special characters from the printed document. It is shown that
this approach can reduce the character-level error rate of Tesseract from 6.03
to 2.61 for Tamil (-3.42% relative change) and 7.61 to 4.74 for Sinhala (-2.87%
relative change), as well as the word-level error rate from 39.68 to 20.61 for
Tamil (-19.07% relative change) and 35.04 to 26.58 for Sinhala (-8.46% relative
change) on the test set. Also, our newly created parallel corpus consists of
185.4k, 168.9k, and 181.04k sentences and 2.11M, 2.22M, and 2.33M Words in
Tamil, Sinhala, and English respectively. This study shows that fine-tuning
Tesseract models on multiple new fonts help to understand the texts and
enhances the performance of the OCR. We made newly trained models and the
source code for fine-tuning Tesseract, freely available.
