Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
machine learning models of handwritten character recognition

machine learning models of handwritten character recognition

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798182724272
Publisher: Savage
Pages: 116
Weight: 0.48
Height: 0.32 Width: 6.00 Depth: 9.00
Language: English

Document Analysis and Recognition (DAR) systems play a critical role in enabling this data exchange. A key component of DAR systems is Optical Character Recognition (OCR), which allows for the recognition of both printed and handwritten text. Handwritten text recognition systems are especially valuable, providing a seamless interface that enhances communication between users and computers, enabling machines to interpret and process handwritten documents. Such systems contribute substantially to bridging the gap between humans and machines. Although considerable research has focused on recognizing characters in Indian scripts, the challenge of efficient data exchange between humans and machines remains unresolved for these scripts. This book addresses these challenges by developing a framework for predicting age, gender, and handedness in handwritten character recognition of the Gurumukhi script, which is used for the Punjabi language, one of India's official languages. Notably, Gurumukhi is the tenth most widely used script globally. It uses feature extraction techniques, performance evaluation metrics in age, gender and handedness, and explores various methods using machine learning models/ neural networks for recognition of indic and non-indic scripts.

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