CTCbased

CTCbased refers to models that use Connectionist Temporal Classification (CTC) loss to train neural networks for sequence labeling without requiring frame-level alignments. The method, introduced by Graves and colleagues, is designed for tasks where the input and output sequences have different lengths and the alignment between them is unknown, such as speech, handwriting, or sign-language recognition. CTC-based models typically output at each time step a probability distribution over a set of labels plus a special blank token, which allows the model to emit repeated labels and to skip inputs.

During training, the CTC loss sums over all possible alignments between the input sequence and the target label sequence, effectively marginalizing the alignments. The blank symbol enables the model to represent non-emitting time steps and to compress repetitions. In decoding, practitioners often use greedy decoding or beam search, sometimes combined with an external language model to improve fluency and accuracy.

CTC-based approaches are common in end-to-end speech recognition, handwriting recognition, OCR, lip-reading, and other sequence tasks. They are typically built with recurrent neural networks such as LSTMs or GRUs, but can also be combined with convolutional networks or modern transformer architectures. They enable real-time streaming models when implemented with suitable architectures.

Advantages of CTCbased methods include training with unsegmented data, end-to-end optimization, and relatively simple alignment handling. Limitations include an implicit independence assumption between output labels and potential difficulty modeling strong dependencies within a label sequence; decoding may require external language models for best results. Alternatives include RNN-Transducer and attention-based models that capture longer-range dependencies differently.