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- W2119845695 abstract "Despite the design challenges, automatic character recognition has been the subject of intensive research. However, when it comes to handwritten character recognition, the challenges are not only daunting but also achieving acceptable accuracy, which is also a big concern. Many machine learning techniques have been applied by various researchers for different scripts and characters. However, in case of Gurumukhi characters (an Indian language), the research is not as advanced and effective as per the latest reviews. In this work, the Hopfield Neural Network (HNN) model is applied for the recognition of the isolated handwritten Gurumukhi digits. A total of 1500 different input patterns have been tested over the network sequentially, and a recognition accuracy of 95.4% has been recorded. All preprocessing activities and implementation of Hopfield model have done using MATLAB." @default.
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- W2119845695 date "2012-02-29" @default.
- W2119845695 modified "2023-09-26" @default.
- W2119845695 title "Recognition of Isolated Handwritten Oriya Numerals using Hopfield Neural Network" @default.
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- W2119845695 doi "https://doi.org/10.5120/4986-7250" @default.
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