Sequence Modeling with CTC
This article explains Connectionist Temporal Classification (CTC), a method for sequence modeling that allows training recurrent neural networks on sequence data without requiring alignment between input and target sequences. It describes how CTC works, its applications in speech recognition and handwriting recognition, and its advantages over traditional approaches.
Background
Connectionist Temporal Classification (CTC) is a neural network output layer and loss function introduced in 2006 by Alex Graves and colleagues. It solves a core problem in sequence modeling: training a model to map an input sequence (e.g., audio frames) to a shorter output sequence (e.g., text characters) when the alignment between them is unknown. Instead of requiring pre-segmented training data where each input frame is labeled, CTC lets the model learn alignments automatically by summing over all possible paths. Crucially, CTC collapses repeated characters and inserts a special "blank" token, enabling it to handle variable-length outputs like speech transcriptions or handwriting. It was instrumental in the rise of end-to-end speech recognition systems (e.g., DeepSpeech by Baidu) and remains foundational to modern ASR, text recognition, and certain music transcription pipelines, though it has been partly superseded by attention-based and transducer models.