Journal ArticleDOI
The optical character recognition of Urdu-like cursive scripts
Saeeda Naz,Khizar Hayat,Muhammad Imran Razzak,Muhammad Waqas Anwar,Sajjad A. Madani,Samee U. Khan +5 more
- Vol. 47, Iss: 3, pp 1229-1248
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.read more
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
Muhammad Attique Khan,Muhammad Sharif,Muhammad Younus Javed,Tallha Akram,Mussarat Yasmin,Tanzila Saba +5 more
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
Saeeda Naz,Arif Iqbal Umar,Riaz Ahmad,Imran Siddiqi,Saad Bin Ahmed,Muhammad Imran Razzak,Faisal Shafait +6 more
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
Saeeda Naz,Arif Iqbal Umar,Riaz Ahmad,Saad Bin Ahmed,Syed Hamad Shirazi,Imran Siddiqi,Muhammad Imran Razzak +6 more
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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