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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Using BLSTM for Interpretation of 2D Languages - Case of Handwritten Mathematical Expressions.
TL;DR: The proposed solution aims at transforming the mathematical expression description into a sequence including at the same time symbol labels and relation- ship labels, so that classical supervised sequence labeling with recurrent neural networks can be applied.
Posted Content
Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code).
TL;DR: The statistical analysis of results indicates that Multinomial Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest and Decision Trees offer similar level of accuracy in comment classification.
Book ChapterDOI
An Efficient Multi Lingual Optical Character Recognition System for Indian Languages Through Use of Bharati Script
TL;DR: Thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for Indian scripts.
Patent
Text emotion classification method based on emotion center
TL;DR: In this article, a text emotion classification method based on an emotion center was proposed, which comprises the steps that first, the distance between a text vector and a category emotion vector center is added into a loss function, and then, a BLSTM is used to perform preliminary decoding on a text, and the accuracy of text representation is improved through an attention mechanism.
Book ChapterDOI
A Performance Evaluation of Several Artificial Neural Networks for Mapping Speech Spectrum Parameters.
TL;DR: The results show that for this application of neural networks, the architectures with more layers or the greater number of neurons are not the most convenient, both for the time required in their training and for the adjustment achieved.
References
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Posted Content
Speech Recognition with Deep Recurrent Neural Networks
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Proceedings ArticleDOI
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