Deep learning based 3D residual convolutional and Multi-Head Attention (3D-RMA) for lip-reading
Results in Control and OptimizationLip reading, an essential yet intricate facet of communication, has seen notable progress through the application of advanced deep learning techniques. This research introduces a deep learning-based lip-reading model that integrates Conv3D layers, Multi ...
Archana Chaudhari +6 more
doaj +1 more source
Terminal strip detection and recognition based on improved YOLOv7-tiny and MAH-CRNN+CTC models
Frontiers in Energy ResearchFor substation secondary circuit terminal strip wiring, low efficiency, less easy fault detection and inspection, and a variety of other issues, this study proposes a text detection and identification model based on improved YOLOv7-tiny and MAH-CRNN+CTC ...
Guo Zhijun +3 more
doaj +1 more source
End-to-end handwritten Ge’ez multiple numerals recognition using deep learning
SICE Journal of Control, Measurement, and System IntegrationGe'ez has been used in Ethiopian churches for centuries to read and interpret the Bible. As a part of the liturgy and religious ceremonies, it is also utilized in prayer and chanting.
Ruchika Malhotra, Maru Tesfaye Addis
doaj +1 more source
IEEE AccessHandwritten Text Recognition (HTR) models often exhibit optimization difficulty when trained with small batch sizes. In this study, we investigate the temporal behavior of CTC logits in Connectionist Temporal Classification (CTC)-based HTR models ...
Pham Doan Tinh, Ha Huu An
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AMHARIC SPEECH RECOGNITION USING JOINT TRANSFORMER AND CONNECTIONIST TEMPORAL CLASSIFICATION WITH CHARACTER-BASED AND SUBWORD-BASED ACOUSTIC AND LANGUAGE MODELS [PDF]
Sequence-to-sequence attention-based models have gained considerable attention in recent times for automatic speech recognition. The transformer architecture has been extensively employed for a variety of sequence-to-sequence transformation problems ...
Zede
core +1 more source
A textual-guided compact visual attention features for continuous sign language recognition
Discover Artificial IntelligenceContinuous Sign Language Recognition (CSLR) datasets contain RGB videos annotated with similar sentence glosses representing sign sequences. However, the performance of CSLR systems is often hindered by issues such as redundant video frames, sparse text ...
V. Prathyusha, P. V. V. Kishore
doaj +1 more source
Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm [PDF]
, 1993 Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through ...
Civelek, Ferda N. (Ferda Nur)
core
Audio Transcription berbahasa Mendarin Menggunakan Connectionist Temporal Classification
, 2020 Bahasa Mandarin menjadi salah satu bahasa yang paling sering dituturkan orang diseluruh dunia. Banyak faktor penting yang perlu dipelajari agar dapat menguasai bahasa Mandarin. Salah satunya adalah kemampuan dalam berkomunikasi.
Jansen, Jansen
core
Bayesian Alignment Prior to Connectionist Temporal Classification Loss
We propose revisions of Connectionist Temporal Classification (CTC) loss and forced alignment to support longer input sequences. CTC loss is normally used for training in speech and handwriting recognition tasks when the labels are not fully aligned with
Sorenson, Lawry
core
A zero-shot LLM framework for multimodal grievance classification, urgency scoring, and abuse detection in civic feedback systems. [PDF]
Sci RepRajkumar SC +3 more
europepmc +1 more source