2021
DOI: 10.1109/access.2021.3123726
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Real-Time Pashto Handwritten Character Recognition Using Salient Geometric and Spectral Features

Abstract: Pashto scripts are cursive in nature and hard to recognize in real-time. Native speakers of the Pashto language are large in numbers and reside in different regions of the world. Due to the cursive nature of the Pashto script along with variations in character strokes, the printed, as well as handwritten characters, are difficult to be detected, classified or recognized. In real-time handwritten character recognition systems, the challenging factors that constraints the system depends on the stroke noise, geom… Show more

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Cited by 16 publications
(5 citation statements)
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“…Extractive and abstractive are the two types of summarization. A subset of sentences from the input text is chosen through extractive summarization to provide a summary [ 2 ]. In contrast, abstractive summarization restructures the language in the text and, if necessary, introduces new words/phrases into the summary.…”
Section: Introductionmentioning
confidence: 99%
“…Extractive and abstractive are the two types of summarization. A subset of sentences from the input text is chosen through extractive summarization to provide a summary [ 2 ]. In contrast, abstractive summarization restructures the language in the text and, if necessary, introduces new words/phrases into the summary.…”
Section: Introductionmentioning
confidence: 99%
“…This difficulty leads to the non-availability of properly collated and annotated data, which is the primary reason for such less research in handwritten Urdu character recognition. Similar challenges also exist for typesetting Persian and Arabic characters, which has prompted an increase in similar research in innovative ways for character recognition for these languages as well [16], [38], [43] . This paper specifically focuses on the recognition of handwritten characters in the Urdu language.…”
Section: Related Work a Urdu Handwritten Character Recognitionmentioning
confidence: 97%
“…Research works have presented numerous methods for categorizing handwritten characters and digits. Handwriting recognition has been previously demonstrated encouraging outcomes utilizing shallow networks [10], [11]. The accuracy rate attained by the MNIST dataset was 91.08% in Hinton et al's research on deep belief networks (DBN), which consist of three layers and incorporate a learning algorithm [12].…”
Section: Related Workmentioning
confidence: 99%