Design of an offiline Ottoman character recognition system for translating printed documents to modern Turkish için kapak resmi
Design of an offiline Ottoman character recognition system for translating printed documents to modern Turkish
Başlık:
Design of an offiline Ottoman character recognition system for translating printed documents to modern Turkish
Yazar:
Küçükşahin, Naz, author.
Yazar Ek Girişi:
Fiziksel Tanımlama:
xi, 75 leaves: charts;+ 1 computer laser optical disc.
Özet:
Optical character recognition (OCR) is one of the most studied topics for many years. As a result of these studies, systems developed especially for the Latin alphabet have become more accurate even for handwritten texts. However, there are very limited studies on Ottoman OCR systems in the literature and it is still a subject of interest due to the complexity of the language in grammar, writing and spelling. In this thesis, it is aimed to design an offline OCR system that recognizes Ottoman characters using deep convolutional neural networks. The proposed work consists of several steps such as image processing, image digitization and character segmentation, adaptation of inputs to the network, training of the network, recognition and evaluation of results. Firstly, a character dataset was created by segmenting text images of different lengths that was selected among scanned samples of various Ottoman literature from the digital database of Turkish National Library. Two convolutional neural networks of different complexity were trained with the created character dataset and the relationship between recognition rates and network complexity was evaluated. Secondly, using the Histogram of Oriented Gradients and Principal Component Analysis, the features of the created dataset were extracted and the Ottoman characters were classified with k-Nearest Neighbor Algorithm and Support Vector Machines which are widely used classification methods in the literature. The performed analyzes have shown that both networks provide acceptable recognition rates compared to the conventional classifiers, however complex deep neural network showed better accuracy and lower loss.
Yazar Ek Girişi:
Tek Biçim Eser Adı:
Thesis (Master)--İzmir Institute of Technology: Electronics and Communication Engineering.

İzmir Institute of Technology: Electronics and Communication Engineering--Thesis (Master).
Elektronik Erişim:
Access to Electronic Versiyon.
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