Handwritten character recognition using cellular neural networks

Duran, Selma


Handwritten digit string segmentation and recognition using deep learning
Elitez, Orçun; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2015)
The main purpose of this thesis is to build a reliable method for the recognition of handwritten digit strings. In order to accomplish the recognition task, first, the digit string is segmented into individual digits. Then, a digit recognition module is employed to classify each segmented digit completing the handwritten digit string recognition task. In this study, a novel method, which uses deep belief networks architecture, is proposed in order to achieve high performance on the digit string segmentation...
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Alpas, Hami; Özilgen, Mustafa; Bozoğlu, Faruk; Department of Food Engineering (1995)
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Enöz, Çetin; Kaftanoğlu, Bilgin; Department of Mechanical Engineering (1994)
Hand-crafted versus learned representations for audio event detection
Kucukbay, Selver Ezgi; Yazıcı, Adnan; Kalkan, Sinan (2022-04-01)
Audio Event Detection (AED) pertains to identifying the types of events in audio signals. AED is essential for applications requiring decisions based on audio signals, which can be critical, for example, for health, surveillance and security applications. Despite the proven benefits of deep learning in obtaining the best representation for solving a problem, AED studies still generally employ hand-crafted representations even when deep learning is used for solving the AED task. Intrigued by this, we investi...
Hand gesture recognition system
Gingir, Emrah; Bulut, Mehmet Mete; Akar, Gözde; Department of Electrical and Electronics Engineering (2010)
This thesis study presents a hand gesture recognition system, which replaces input devices like keyboard and mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-colored glove or need lots of training data. The system mentioned ...
Citation Formats
S. Duran, “Handwritten character recognition using cellular neural networks,” Middle East Technical University, 1995.