System Architecture for CTC

System architecture for Connectionist Temporal Classification (CTC) involves a neural network that processes sequential data, like speech or handwriting, to recognize patterns without needing exact alignments. It typically includes input layers that capture data features, hidden layers that learn representations, and an output layer producing probabilities for all possible labels. A decoding algorithm then interprets these probabilities to generate the most likely sequence of outputs, handling timing variations. This structure enables flexible, efficient sequence recognition, making CTC suitable for tasks like speech-to-text and handwriting recognition.