DOWNLOAD JAMEEL NOORI NASTLIQ
VMC TECHNICALApril 21, 2020
JAMEELNOORI NASTLIQ AND NOORI KASHEEDA FONTS
History about this calligraphy
Ahmad Mirza Jamil (Urdu: احمد مرزا جمیل; 21 February 1921 – 17 February 2014) was a Pakistani calligrapher best known for creation of Noori style of Nastaliq, which was first created as a digital typeface (font, Noori Nastaliq) in 1981.
Urdu OCR’s have been an object of interest for many developers in the recent years. Active research is being done pertaining to Urdu OCR’s, but because of the complexity associated with Urdu fonts; it still lacks perfection halting it from coming up to the surface. The main objective was to create a technique that could be applied to any of the existing Urdu fonts/scripts. In this paper, the authors have developed a technique which is capable of extracting the Urdu font “Jameel Noori Nastaleeq” from images and converts it into editable textual Unicodes. The approach comprises of pre-processing techniques, label connected components, feature extraction, and image comparison. The identified objects are saved as templates which are then compared to the white pixel position length database created by the authors in order to identify the templates which are then converted into Unicode.
History about this calligraphy
Ahmad Mirza Jamil (Urdu: احمد مرزا جمیل; 21 February 1921 – 17 February 2014) was a Pakistani calligrapher best known for creation of Noori style of Nastaliq, which was first created as a digital typeface (font, Noori Nastaliq) in 1981.
Urdu OCR’s have been an object of interest for many developers in the recent years. Active research is being done pertaining to Urdu OCR’s, but because of the complexity associated with Urdu fonts; it still lacks perfection halting it from coming up to the surface. The main objective was to create a technique that could be applied to any of the existing Urdu fonts/scripts. In this paper, the authors have developed a technique which is capable of extracting the Urdu font “Jameel Noori Nastaleeq” from images and converts it into editable textual Unicodes. The approach comprises of pre-processing techniques, label connected components, feature extraction, and image comparison. The identified objects are saved as templates which are then compared to the white pixel position length database created by the authors in order to identify the templates which are then converted into Unicode.
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