Proceedings ArticleDOI
Text recognition using deep BLSTM networks
Anupama Ray,Sai Rajeswar,Santanu Chaudhury +2 more
- pp 1-6
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TLDR
A Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition that uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment.Abstract:
This paper presents a Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition. This architecture uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment. This work is motivated by the results of Deep Neural Networks for isolated numeral recognition and improved speech recognition using Deep BLSTM based approaches. Deep BLSTM architecture is chosen due to its ability to access long range context, learn sequence alignment and work without the need of segmented data. Due to the use of CTC and forward backward algorithms for alignment of output labels, there are no unicode re-ordering issues, thus no need of lexicon or postprocessing schemes. This is a script independent and segmentation free approach. This system has been implemented for the recognition of unsegmented words of printed Oriya text. This system achieves 4.18% character level error and 12.11% word error rate on printed Oriya text.read more
Citations
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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.
Journal ArticleDOI
Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks
TL;DR: An attempt is made to systematically analyze trends in the development of approaches and detection methods, reasons behind these developments, as well as metrics designed to assess the quality and reliability of object detection.
Journal ArticleDOI
Urdu Nasta’liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks
Saeeda Naz,Saeeda Naz,Arif Iqbal Umar,Riaz Ahmed,Muhammad Imran Razzak,Sheikh Faisal Rashid,Faisal Shafait +6 more
TL;DR: Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta’liq writing style achieves a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.
Journal ArticleDOI
Segmentation-free optical character recognition for printed Urdu text
TL;DR: A segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition using Hidden Markov Models for classification is presented.
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Action Recognition From Thermal Videos
TL;DR: This study proposes the cycle-consistent generative adversarial network (CycleGAN)-based methods for removing halo effects from thermal images and restoring the areas of the human bodies and considered a method for creating a skeleton image from a thermal image to analyze body movements.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Proceedings ArticleDOI
Speech recognition with deep recurrent neural networks
TL;DR: This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs.
Posted Content
Speech Recognition with Deep Recurrent Neural Networks
TL;DR: In this paper, deep recurrent neural networks (RNNs) are used to combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs.
Proceedings ArticleDOI
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
TL;DR: This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.