The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques.
Apr 21, 2023
DeepNet-WI: a deep-net model for offline Urdu writer identification. ST Nabi, M Kumar, P Singh. Evolving Systems 15 (3), 759-769, 2024. 7, 2024 ; A convolution ...
DeepNet-WI: a deep-net model for offline Urdu writer identification · Syed ... An offline Urdu handwritten writer identification system using a deep learning ...
DeepNet-WI: A deep-net model for offline Urdu writer identification. ... UrduDeepNet: Offline handwritten Urdu character recognition using deep neural network.
May 31, 2024 · This paper presents a general approach for identification documents classification using deep learning models. Our study gives an explanation of ...
DeepNet-WI: a deep-net model for offline Urdu writer identification. Evol ... Writer Identification from Offline Handwriting Images in Urdu Script with Dense-Net: ...
This paper proposes a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns, ...
Aug 10, 2024 · Nabi S.T., Kumar M., Singh P. DeepNet-WI: a deep-net model for offline urdu writer identification. Evol. Syst., 15 (3) (2024), pp. 759-769.
... DeepNet-WI: a deep-net model for offline Urdu writer identification. Evolving Systems. 15:759–769. https://doi.org/10.1007/S12530-023-09504-1. Ahmed S Bin ...
DeepNet-WI: a deep-net model for offline Urdu writer identification. Syed Tufael Nabi; Munish Kumar; Paramjeet Singh. Original Paper 21 April 2023 Pages: 759 ...