Image character through signal and pattern formation

Zaman, Safdar; Anwar, Kanwal; Khan, Riaz Ullah

doi:10.1109/lt.2016.7562865

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…For the features extraction from the picture, Support Vector Machine (SVM) is used as a classifier for multi-arranged content/non-content and Recurrent Neural Network (RNN) based classifier is utilized for recognition of content characters. Safdar Zaman et al [16] presented a method that works by changing the image into signal and after that follow the similar pattern of the image in respective signal format. The approach works under five stages which are shows specifically.…”

Section: Figure 7 Shows the Comparison Of Urdu Script Classifiersmentioning

confidence: 99%

An Analysis of Optical Character Recognition (OCR) Methods

Ashraf¹,

Arafat²,

Iqbal³

2019

International Journal of Computational Linguistics Research

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This survey paper presents a comprehensive study of Urdu Optical Character Recognition (OCR) methodologies. The main focus of the study is detail investigation of the techniques used to recognize the Nastaliq, Naskh and other similar scripts fonts. These script fonts are used to write Urdu, Arabic, Pashto and Sindhi etc. languages. Several methods of text recognition and classification of Urdu like cursive scripts are discussed. The survey contains the comparison and description of each method in a brief way which identifies handwritten, printed and online text recognition as well. For each optical character recognition (OCR) the phases of pre-processing, segmentation, feature extraction, classification and finally recognition are discussed. After the comprehensive analysis of all methodologies critics and future work in Urdu cursive scripts, i.e. Naskh and Nastaliq scripts are also proposed.

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Section: Figure 7 Shows the Comparison Of Urdu Script Classifiersmentioning

confidence: 99%