Proceedings ArticleDOI
Text recognition using deep BLSTM networks
Anupama Ray,Sai Rajeswar,Santanu Chaudhury +2 more
- pp 1-6
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
Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network
TL;DR: The proposed gait-based human identification based on the front and back view images of humans captured in both high- and low-illumination environments outperforms previous methods.
Journal ArticleDOI
Multiobjective optimization for recognition of isolated handwritten Indic scripts
TL;DR: A modified opposition-based multiobjective Harmony Search algorithm has been proposed to select the local regions from handwritten character images based on their rankings in a three-dimensional pareto-front based on recognition accuracy and redundancy.
Proceedings ArticleDOI
KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark
TL;DR: This paper presents the first Pashto text image database for scientific research and thereby the first dataset with complete handwritten and printed text line images which ultimately covers all alphabets of Arabic and Persian languages.
Journal ArticleDOI
In-air handwritten English word recognition using attention recurrent translator
Ji Gan,Weiqiang Wang +1 more
TL;DR: An attention-based model, called attention recurrent translator, is proposed for the in-air handwritten English word recognition, which is considerably different from connectionist temporal classification (CTC) and achieves a word recognition accuracy of 97.74%.
Journal ArticleDOI
Improving the DBLSTM for on-line Arabic handwriting recognition
Rania Maalej,Monji Kherallah +1 more
TL;DR: This work proposes a new online Arabic handwriting recognition system based on DBLSTM that relies on three techniques that were applied in order to enhance its performance, and the best tested architecture gives a reduction of 10.99% in label error rate.
References
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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
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