T&amp;#x00FC;b&amp;#x0131;tak Turkish &amp;#x2014; Ottoman handwritten recognition system

2023

IJDAR

Ottoman script is an Arabic alphabet-based script as well. It was a writing system of the Turkish language for several centuries until it was replaced with the modern Turkish script, which is based on the Latin alphabet, in 1928. With the ever increasing digitization campaigns, millions of Ottoman documents are coming to light. But, their contents are not directly accessible, nor they are digitally editable and searchable. OCR and text recognition technologies can be a solution to this problem in the form of auto-mated and semi-automated conversion systems. This study 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 0.16 CER on a test set containing line images from a historical printed Ottoman book.

Section: Arabic Text Recognitionmentioning

confidence: 99%

Printed Ottoman text recognition using synthetic data and data augmentation

2023

IJDAR

“…Most of them are dated to pre-deep learning era and use traditional machine learning techniques [12,17,18,26]. In a study that used deep learning techniques on Ottoman documents for the first time, Aydemir et al trained an RNN system by manually extracting features from a dataset containing 169,148 discrete handwritten word images obtained from population registration documents [13]. The accuracy is reported as 12.4% character error rate and 22.1% word error rate on a small test set of 1,000 different words.…”

Section: Related Workmentioning

confidence: 99%

Transcription of Ottoman Machine-Print Documents

Kizilirmak

Akcan

et al. 2022

Preprint

With the ever increasing speed of the digitization process, a large collection of Ottoman documents is accessible to researchers and the general public. But, the majority of the users interested in these documents can not read these documents unless they are transcripted to the modern Turkish script which use an extended version of the Latin alphabet. Manual transcription of such a massive amount of documents is beyond the capacity of human experts. As a solution, we propose an automatic recognition system for printed Ottoman documents which transcribes Ottoman texts directly to the modern Turkish script. We evaluated three decoding strategies including the Word Beam Search decoder that allows to use a recognition lexicon and n-gram statistics during the decoding phase. The system achieves 2.25% character error rate and 6.42% word error rate on a test set of 1.4K samples, using the test set transcriptions as the recognition lexicon. Using a general purpose, large lexicon of the Ottoman era (260K words and 77% test coverage), the performance is measured as 3.68% character error rate and 16.61% word error rate.

Printed Ottoman Text Recognition Using Synthetic Data and Data Augmentation

2022

Preprint

Ottoman script is an Arabic alphabet-basedscript as well. It was a writing system of theTurkish language for several centuries until it was replaced with the modern Turkish script,which is based on the Latin alphabet, in 1928. With the ever increasing digitization campaigns, millions of Ottoman documents are coming to light. But, their contents are not directly accessible, nor they are digitally editable and searchable. OCR and text recognition technologies can bea solution to this problem in the form of auto-mated and semi-automated conversion systems. This study presents a DL-based characterrecognition 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 0.16 CER on a test set containing line images from a historical printed Ottoman book.