2021
DOI: 10.22581/muet1982.2101.14
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Abstract: This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten P… Show more

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Cited by 14 publications
(3 citation statements)
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“…In order to improve the efficiency and quality of collecting end-to-end OCR character features of power equipment nameplates, the data are classified, the laws and the actual situation in the collection process are analyzed, and the corresponding data features are summarized, which lays a basic environment for the application and collection of end-toend OCR character features of power equipment nameplates [5][6]. Template matching is to use predefined templates to find and match areas similar to templates in images.…”
Section: End-to-end Ocr Character Recognition Of Power Equipment Name...mentioning
confidence: 99%
“…In order to improve the efficiency and quality of collecting end-to-end OCR character features of power equipment nameplates, the data are classified, the laws and the actual situation in the collection process are analyzed, and the corresponding data features are summarized, which lays a basic environment for the application and collection of end-toend OCR character features of power equipment nameplates [5][6]. Template matching is to use predefined templates to find and match areas similar to templates in images.…”
Section: End-to-end Ocr Character Recognition Of Power Equipment Name...mentioning
confidence: 99%
“…26), histogram of oriented gradients (HoG), and location invariant features are proposed to calculate the feature map. In the X-ray images, most of the time, the resultant images are blurred, scaled, rotated, and many others and most of the feature extraction techniques fail in accumulating optimum astute value in such cases, but invariant feature extraction techniques are prominent in these cases because their feature accumulation capabilities are never affected by rotation and scaling (27).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Some of the researchers performed case studies to inform people about FinTech applications and flaws in the traditional business models. While some researchers inspired from the extensive applications of machine learning techniques in diverse domains (healthcare, 124 internet security, [125][126][127] text recognition domain, [128][129][130] and many others) suggested machine learning models during the development of FinTech-driven applications to analyze the sentiments of the population regarding FinTech applications. List of other models and other techniques suggested are depicted in Table 4 below.…”
Section: Based On the Extant What Are Different Techniques Suggested ...mentioning
confidence: 99%