Combination of Auto-encoder architecture and super resolution for better segmentation of thinned and cursive handwritten documents
In neural networks an auto-encoder architecture has several applications such as image denoising, feature reduction, data compression, image colorization, dimensanality reduction, segmentation and so on. Super-resolution is used to upgrade the low resolution images into high resolution. In order to...
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| Veröffentlicht in: | Journal of physics. Conference series 2022-03, Vol.2236 (1), p.12007 |
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| container_title | Journal of physics. Conference series |
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| creator | MP, Ayyoob Ilyas.P, Muhamed |
| description | In neural networks an auto-encoder architecture has several applications such as image denoising, feature reduction, data compression, image colorization, dimensanality reduction, segmentation and so on. Super-resolution is used to upgrade the low resolution images into high resolution. In order to get a better result on segmentation of thinned hand written images, this paper proposes a method of combination of associative auto-encoder architecture and super resolution for pixel expansion.
Experimental results show that the combination of proposed network and super resolution method accurately segments the thinned handwritten Arabic words. |
| doi_str_mv | 10.1088/1742-6596/2236/1/012007 |
| format | Article |
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| subjects | Coders Data compression Handwriting Image compression Image resolution Image segmentation Neural networks Physics Reduction |
| title | Combination of Auto-encoder architecture and super resolution for better segmentation of thinned and cursive handwritten documents |
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