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- W3119891751 abstract "Neural computers' computational operations are employed in a strategic way that is entirely disparate from that of standard conventional computers. Neural computers are primarily computational train tutored there by if given a definite precise original state (data input), they whatsoever classify the input data should be rolled into one of the well known categorized classes or convicts that raw input data to yield into a specific worthwhile advantageous property is improved. In this system, we pass small patches of handwritten images to convolution neural networks, and it recognizes the text from the given input images. Neural computers implement data parallelism. The main objective of this paper is to propose the design of an expert knowledge-based neural network system for handwritten recognition that can effectively recognize the text from the given input image using recurrent neural network approach. This approach can be used as an optimal solution tool for handwritten recognition as it provides faster classification with more accuracy." @default.
- W3119891751 created "2021-01-18" @default.
- W3119891751 creator A5044565568 @default.
- W3119891751 creator A5074624246 @default.
- W3119891751 date "2020-12-16" @default.
- W3119891751 modified "2023-09-23" @default.
- W3119891751 title "Text-Based Handwritten Recognition Through an Image Using Recurrent Neural Network" @default.
- W3119891751 cites W2112274905 @default.
- W3119891751 cites W2326198886 @default.
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- W3119891751 doi "https://doi.org/10.1007/978-981-15-8354-4_47" @default.
- W3119891751 hasPublicationYear "2020" @default.
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