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
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Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction
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A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition
TL;DR: A multi-objective region sampling methodology for isolated handwritten Bangla characters and digits recognition has been proposed and an AFS theory based fuzzy logic is utilized to develop a model for combining the pareto-optimal solutions from two multi- objective heuristics algorithms.
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A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts
TL;DR: In the present work, a non-explicit feature based approach, more specifically, a multi-column multi-scale convolutional neural network (MMCNN) based architecture has been proposed for this purpose and a deep quad-tree based staggered prediction model has be proposed for faster character recognition.
References
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Proceedings Article
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves,Jürgen Schmidhuber +1 more
TL;DR: In this article, a modified, full gradient version of the LSTM learning algorithm was used for framewise phoneme classification, using the TIMIT database, and the results support the view that contextual information is crucial to speech processing, and suggest that bidirectional networks outperform unidirectional ones.
Journal ArticleDOI
Online and off-line handwriting recognition: a comprehensive survey
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Journal ArticleDOI
2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves,Jürgen Schmidhuber +1 more
TL;DR: In this article, a modified, full gradient version of the LSTM learning algorithm was used for framewise phoneme classification, using the TIMIT database, and the results support the view that contextual information is crucial to speech processing, and suggest that bidirectional networks outperform unidirectional ones.
Book
Supervised Sequence Labelling with Recurrent Neural Networks
TL;DR: A new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.
Journal ArticleDOI
A Novel Connectionist System for Unconstrained Handwriting Recognition
TL;DR: This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies, significantly outperforming a state-of-the-art HMM-based system.