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Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks
2013
2013 12th International Conference on Document Analysis and Recognition
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In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. ...
Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. ...
In this paper, we demonstrate the application of 1D bidirectional LSTM networks to the printed Urdu Nastaleeq recognition. 1D BLSTMs are different than 2D or multidimensional BLSTMs in how input sequence ...
doi:10.1109/icdar.2013.212
dblp:conf/icdar/Ul-HasanARSB13
fatcat:dxbu2g2udrb5vnsodibzma5sky
MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning
2021
IEEE Access
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That is, for any LSTM cell at timestamp t, the context from cells 1 to t-1 are carried out to this cell. Thus, Bidirectional LSTM is introduced here. ...
To that end, we have developed a printed Urdu text corpus along with the corresponding OCR benchmark. ...
doi:10.1109/access.2021.3110787
fatcat:ztxzqok6mncvtattpf7dyzbnvy
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
[article]
2015
arXiv
pre-print
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Deep LSTM is an ideal candidate for text recognition. ...
This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the ...
This framework is very “Offline printed urdu nastaleeq script recognition with bidirectional lstm
effective in case of insertion and deletion errors introduced networks ...
arXiv:1502.07540v1
fatcat:lmptvd5wxfcmhddkhh5bkn44ue
CALText: Contextual Attention Localization for Offline Handwritten Text
[article]
2021
arXiv
pre-print
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Recognition of Arabic-like scripts such as Persian and Urdu is more challenging than Latin-based scripts. ...
Not much research exists for offline handwritten Urdu script which is the 10th most spoken language in the world. ...
and 87.6% accuracy for Urdu Nastaleeq style. ...
arXiv:2111.03952v1
fatcat:6fdcbrwpv5gchmxak7en6r77ri
Urdu Handwritten Text Recognition: A Survey
2020
IET Image Processing
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In this study, the authors presented a comprehensive survey for a number of offline and online handwritten text recognition systems for Urdu script written in Nastaliq font style from 2004 to 2019. ...
Work on the problem of handwritten text recognition in Urdu script has been an active research area. A significant progress is made in this interesting and challenging field in the last few years. ...
of both the printed and handwritten Urdu Nastaleeq text based on ligature information. ...
doi:10.1049/iet-ipr.2019.0401
fatcat:vm6xhbrulvhrnj6eglcydaxwwe
A BLSTM Network for Printed Bengali OCR System with High Accuracy
[article]
2019
arXiv
pre-print
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A good Indic multi script OCR system is also developed by Google. ...
This paper presents a printed Bengali and English text OCR system developed by us using a single hidden BLSTM-CTC architecture having 128 units. ...
We also plan to include Assamese script in our system. Moreover, we intend to enhance our system to recognize the Bengali text printed in obsolete Lino-Monotype fonts. ...
arXiv:1908.08674v1
fatcat:oi3tkwqndbgmzewpjufvu42rri
Amharic OCR: An End-to-End Learning
2020
Applied Sciences
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In this paper, we introduce an end-to-end Amharic text-line image recognition approach based on recurrent neural networks. ...
This script uses 34 consonant characters with the seven vowel variants of each (called basic characters) and other labialized characters derived by adding diacritical marks and/or removing parts of the ...
recognition [29] , Urdu Nastaleeq script recognition using bidirectional LSTM [30] , segmentation-free Chinese handwritten text recognition [31] , a hybrid Convolutional Long-Term Memory Network(CLSTM ...
doi:10.3390/app10031117
fatcat:4gtjsm5gcjggtlvfsymqurapgm
Offline Hand Written Urdu Word Spotting using Random Data Generation
2020
IEEE Access
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The system achieved a promising recognition rate of 98.96% due to the sample generation using Cycle-GANs. INDEX TERMS Word spotting, HOG features, hand written text, LSTM, GANs. ...
For the word spotting process, Histogram of Oriented Gradients (HOG) features are extracted from ligature images and then used to train a Long Short-Term Memory (LSTM) network for the classification task ...
This provides a better recognition rate when trained on LSTM network. ...
doi:10.1109/access.2020.3010166
fatcat:ybwmfq7dfnecvkiz7czszbnd5y