PhD Dissertation Defense:Esma Fatıma Bilgin Taşdemir
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A Large Vocabulary Online Handwriting

Recognition System for Turkish


Esma Fatıma Bilgin Taşdemir
Computer Science and Engineering, PhD Dissertation, 2018


Thesis Jury

Prof. Dr Berrin Yanıkoğlu (Thesis Advisor), Prof. Dr. Tunga Güngör,

Assoc. Prof. Dr. Mehmet Göktürk,  Assist. Prof. Dr. Kamer Kaya,

Assist. Prof. Dr. Hüseyin Özkan



Date & Time: 10th, 2018 –  13:00-16:00 PM

Place: L067

Keywords : Hidden Markov Models, Online Handwriting Recognition, Delayed Strokes,

Turkish Handwriting Recognition




Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words.

Based on the Hidden Markov Models, the system comprises pre-processing, feature extraction, optical modeling and language modeling modules. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system.


Turkish script has many similarities with other the Latin scripts like English which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes which introduce an extra source of variation in the sequence order of the handwritten input and high Out-of-Vocabulary (OOV) rate of Turkish when words are used as vocabulary units in the decoding process. This work examines the problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly.