Akis: Transcription Software of Ottoman-Turkish Texts Using Deep Learning

Akis:Transcription Software of Ottoman-Turkish Texts Using Deep Learning Aysu Akcan (bio), Esma Fatıma Bilgin Taşdemir (bio), Fırat Kızılırmak (bio), Mehmet Kuru (bio), Fatma Öncel (bio), and Berin Yanıkoğlu (bio) KEYWORDS Ottoman-Turkish, Printed Texts, Artificial Intelligence, Deep Learning, Text-Recognition Software 1. Introduction Ottoman Turkish1 (OT), which is written in the Arabic and Persian (Arabo-Persian) alphabet, was the language of the Ottoman Empire between the fourteenth and the early twentieth centuries.2 By the nineteenth century, handwritten documents were gradually replaced by the use of printing methods. After the 1928 language reform, OT and its writing system progressively disappeared.3 Researchers working on the Ottoman Empire and the early republican era generally work with primary sources written in OT. They often transcribe OT texts manually into Latin-based scripts before analyzing their content. [End Page 43] Currently, the rapidly evolving field of OT text recognition seeks to enhance the working conditions of researchers and keep up with the demands of the digital age. Akis aims to use AI-based text image recognition methods to develop software and a web application for the automatic transcription of OT texts. While there are many other projects and initiatives capable of providing models for automatic transcription, the most important feature of Akis is that it can be operated without the separate development of transcription models. Furthermore, the decision of the software development team to focus primarily on the nesih writing style sets it apart from other initiatives and projects. The remainder of this research note discusses the rationale for this decision, the scope of the datasets we will use in the development of the Akis software, and the methods we plan to follow. 2. Datasets We use a deep learning (DL) approach in the Akis project. DL techniques require large amounts of data to train deep neural networks. Therefore, the project will devote considerable time and energy to amassing a large collection of text images and annotating them at the line level. The Font and the Writing Style: The initial goal of Akis is to produce automatic transcription of printed texts with nesih font. Nesih became a preferred writing style for OT beginning with the scribal traditions of the early modern period, and the majority of the printed texts from the eighteenth century onwards are written in this style.4 The project's focus is currently restricted to nesih because the software cannot handle the variances in handwritten texts that the multiplicity of Ottoman scribal traditions produced. Datasets consist of printed material composed to teach the software the nesih writing style's visual features. This print-text-based model will assist in reaching the second goal of Akis: the automatic transcription of handwritten texts composed in the nesih writing style. To achieve the goal of AI-assisted automatic transcription of handwritten texts in nesih, the software will be trained by combining data similar to printed material with variations of the nesih handwriting styles. [End Page 44] Content: The data sources to be used for this project are grouped under three categories: the late printing press era (mid-nineteenth century–1928),5 the early printing press era (1727–1848),6 and texts from the early printing press era (fifteenth–eighteenth centuries). We employ criteria emanating from the distinctive patterns of OT texts in terms of the centuries covered, textual diversity, linguistic aspects, etc. to select training data sources. Layout: Page layouts vary in both handwritten and printed texts. Given that the primary aim of the project is transcription, documents originally organized in lines or easily segmented into lines are included in the dataset, while those with complex layouts are excluded. Resolution: Image resolution is also important in the success of character-image recognition systems. Following the standards in the field, we selected texts with a minimum image resolution of 300 dpi.7 3. Workflow and Methodology 3.1. Page and Line Segmentation and Image Processing Segmenting data images into pages, text blocks, lines, words, and non-text areas are usually the first steps of text image recognition. Akis will automatically segment pages into lines. As for complex layouts containing multiple text blocks and...