2020
DOI: 10.3390/s20205884
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Recognition of Pashto Handwritten Characters Based on Deep Learning

Abstract: Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network… Show more

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Cited by 20 publications
(10 citation statements)
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References 53 publications
(39 reference statements)
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“…is method can effectively reduce the dependence of data on shooting angles. Amin et al first selected four bone key points, and then calculated the geometric vector between all other bone joint points and the four key joint points in the cylindrical coordinate system as the frame-level feature vector [17].…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…is method can effectively reduce the dependence of data on shooting angles. Amin et al first selected four bone key points, and then calculated the geometric vector between all other bone joint points and the four key joint points in the cylindrical coordinate system as the frame-level feature vector [17].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Receive the 1D feature vector output by full connection, and then use Softmax classifier to complete the mapping from eigenvalue to probability. If the category of the classification task is K, Softmax maps the input Y ∈ R n to K (0, 1) real numbers and ensures that their sum is 1, as shown in equation (17). Assuming that the output is P ∈ R k , the mapping expression is shown in the following equation.…”
Section: Output Layermentioning
confidence: 99%
“…In the case of human activity recognition, the deep models require large training data; to tackle this problem, the transfer learning approach has been thoroughly studied [29]. Since many promising outcomes have been obtained, a widely agreed issue is that it is very costly to annotate all the human activities, as human annotators and deep learning experts offer numerous efforts, and the probability of error still remains high [30,31]. Nevertheless, providing reliable and Sensors 2020, 20, 7115 5 of 25 relevant details is a salient task in human physical activity detection.…”
Section: Related Workmentioning
confidence: 99%
“…In addition to that, the development of datasets becomes more challenging while it deals with a language whose research is just in its introductory phase [7]. Further, the researchers always try to find out an appropriate dataset that covers all the possible levels of diversity in the target language [12], [13]. Handwritten text recognition has become an important area in the DIA and finding a good dataset for recognition is the main issue.…”
Section: Introductionmentioning
confidence: 99%