Explainable Connectionist-Temporal-Classification-Based Scene Text Recognition [PDF]
Journal of Imaging, 2023 Connectionist temporal classification (CTC) is a favored decoder in scene text recognition (STR) for its simplicity and efficiency. However, most CTC-based methods utilize one-dimensional (1D) vector sequences, usually derived from a recurrent neural ...
Rina Buoy +3 more
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Integrating international Chinese visualization teaching and vocational skills training: leveraging attention-connectionist temporal classification models [PDF]
PeerJ Computer ScienceThe teaching of Chinese as a second language has become increasingly crucial for promoting cross-cultural exchange and mutual learning worldwide. However, traditional approaches to international Chinese language teaching have limitations that hinder ...
Yuan Yao, Zhujun Dai, Muhammad Shahbaz
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Information, 2021 Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages.
Eshete Derb Emiru +4 more
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Advancing Connectionist Temporal Classification with Attention Modeling [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Accepted at ICASSP ...
Jinyu Li
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Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling [PDF]
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 5 pages, 3 figures ...
Vidhyasaharan Sethu +2 more
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A CTC-Based Speech Recognition Network Fusing Local Convolution and Global Attention [PDF]
SensorsIntegrating wav2vec 2.0 with Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) often involves a trade-off between capturing global semantic consistency and maintaining local feature discriminability.
Huijuan Hu +3 more
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Training LDCRF model on unsegmented sequences using connectionist temporal classification [PDF]
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 2016 Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent-dynamic conditional random field (LDCRF) is a well-known discriminative method that has been successfully used for this task.
Sayed Kamaledin Ghiasi-Shirazi +2 more
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MPSA-Conformer-CTC/Attention: A High-Accuracy, Low-Complexity End-to-End Approach for Tibetan Speech Recognition [PDF]
SensorsThis study addresses the challenges of low accuracy and high computational demands in Tibetan speech recognition by investigating the application of end-to-end networks. We propose a decoding strategy that integrates Connectionist Temporal Classification
Changlin Wu +3 more
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SqueezeCall: nanopore basecalling using a Squeezeformer network [PDF]
GigaByteNanopore sequencing, a third-generation sequencing technique, enables direct RNA sequencing, real-time analysis, and long-read length. Nanopore sequencers measure electrical current changes as nucleotides pass through nanopores; a basecaller identifies ...
Zhongxu Zhu
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Brain-Inspired Polymer Dendrite Networks for Morphology-Dependent Computing Hardware. [PDF]
Adv Sci (Weinh)Although process variability is often perceived as a drawback in electronics, this work harnesses the stochastic nature of electropolymerization as a powerful ally for computation. The resulting conductive polymer dendrites exhibit unique structure‐property relationships and support in memory computing, paving the way for the development of a new class
Scholaert C +3 more
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