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Journal ArticleDOI

The optical character recognition of Urdu-like cursive scripts

TLDR
The Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts, with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition in OCR.
Abstract
We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition. HighlightsA literature review of the Nasta'liq and Naskh cursive script OCR.The peculiarities and challenges are described a priori.Printed, handwritten and online OCR efforts are being explored.Analyses based on the stages of a typical OCR pipeline.

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Citations
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Journal ArticleDOI

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

TL;DR: This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Journal ArticleDOI

License number plate recognition system using entropy-based features selection approach with SVM

TL;DR: Simulation results reveal that the proposed method performs exceptionally better compared with existing works, and different performance measures are considered.
Journal ArticleDOI

Urdu Nastaliq recognition using convolutionalrecursive deep learning

TL;DR: This work presents a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script using the proposed hierarchical combination of CNN and MDLSTM.
Posted Content

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

TL;DR: In this paper, a systematic literature review (SLR) is presented to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions, which serve the purpose of presenting state of the art results and techniques on OCR.
Journal ArticleDOI

Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks

TL;DR: An implicit segmentation based recognition system for Urdu text lines in Nastaliq script that relies on sliding overlapped windows on lines of text and extracting a set of statistical features is presented.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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Self-Organizing Maps

TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Book ChapterDOI

Introduction to Algorithms

Xin-She Yang
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
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