Offline Recognition of Handwritten Urdu Characters using B Spline Curves: A Survey

Jameel, Mohd; Kumar, Sanjay

doi:10.5120/ijca2017912604

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Ahmed et al [26] presented an algorithm for Urdu character recognition using bidirectional long short-term memory (BLSTM) on the Urdu nasta'liq handwritten dataset (UNHD). Jameel and Kumar proposed basis spline (Bspline) curves for Urdu character recognition [27]. Nawaz et al [28] compared siamese and triplet networks and showed performance improvement when combined with a CNN for handwritten Urdu character recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Isolated Handwritten Pashto Character Recognition Using a K‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

et al. 2021

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Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.

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Section: Literature Reviewmentioning

confidence: 99%

Isolated Handwritten Pashto Character Recognition Using a K‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

et al. 2021

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“…Jameel et al [23] discovered that different attributes, for example, feature extraction, pre-processing, division and acknowledgment procedures have been utilized and revealed diverse precision levels however the utilization of B-Spline Curve has not been found for manually written Urdu content to shape the feature vector in spite of their strength. We have proposed a procedure in such manner with a specific end goal to improve the exactness and proficiency of Urdu OCR.…”

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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“…Boufenar et al [8] presented the concept of supervised learning technique named Artificial immune system based on zoning technique for isolated carved Arabic letters recognition. Jameel and Kumar [9] suggested the use of B spline curves as a feature extractor for offline Urdu character recognition. Naz et al [10] [11] presented the use of multi-dimensional recurrent Neural Network based on statistical features for Urdu Nastaliq text recognition.…”

Section: Related Workmentioning

confidence: 99%

KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

Khan¹,

Ali²,

Ullah³

et al. 2018

ijacsa

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This paper presents a recognition system for handwritten Pashto letters. However, handwritten character recognition is a challenging task. These letters not only differ in shape and style but also vary among individuals. The recognition becomes further daunting due to the lack of standard datasets for inscribed Pashto letters. In this work, we have designed a database of moderate size, which encompasses a total of 4488 images, stemming from 102 distinguishing samples for each of the 44 letters in Pashto. The recognition framework uses zoning feature extractor followed by K-Nearest Neighbour (KNN) and Neural Network (NN) classifiers for classifying individual letter. Based on the evaluation of the proposed system, an overall classification accuracy of approximately 70.05% is achieved by using KNN while 72% is achieved by using NN.

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Offline Recognition of Handwritten Urdu Characters using B Spline Curves: A Survey

Cited by 6 publications

References 27 publications

Isolated Handwritten Pashto Character Recognition Using a K‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

Isolated Handwritten Pashto Character Recognition Using a K‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

An Analysis of Optical Character Recognition (OCR) Methods

KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

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