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CTC Decoding

CTC (Connectionist Temporal Classification) decoding is a technique used in sequence-to-sequence tasks, such as speech and optical character recognition (OCR), where the alignment between input and output sequences is not given during training. CTC loss is commonly employed to train models for such tasks. During decoding, the goal is to find the most likely output sequence given the model's predictions.

Algorithm

  1. The input to CTC decoding is a sequence of probability distributions produced by the model for each time step. In the context of OCR, this would be the output of a character recognition model applied to an image.
  2. Convert the probabilities into a sequence of characters or symbols. This can be done by selecting the character with the highest probability at each time step.
  3. In the CTC context, it is common to introduce a special "blank" label denoted as "-", which represents gaps between characters. During decoding, duplicate characters and blank labels are typically removed or collapsed.
  4. Remove consecutive blank labels, leaving only a single blank between characters. This is done to ensure that adjacent characters are not merged into a single character.
  5. Merge repeated characters to obtain the final output sequence. This involves removing consecutive duplicate characters, leaving only one instance.
  6. The resulting sequence represents the decoded output. In OCR, this would be the recognized text.

Decoding Types

During decoding, the goal is to transform the model's output (probability distributions over characters for each time step) into the final sequence of characters. Different decoding algorithms can be employed to achieve this. Here are some common types of decoding approaches in CTC

Greedy Decoding

  • In Greedy Decoding, at each time step, the most likely character is selected as the output without considering future time steps.
  • The output sequence is obtained by choosing the character with the highest probability at each time step.
  • Greedy Decoding is simple but may not always yield the most accurate results, especially when there are uncertainties in the model's predictions.
  • Beam Search is a more advanced decoding algorithm that considers multiple hypotheses simultaneously.
  • It maintains a set of candidate hypotheses (the beam) and expands them at each time step based on the model's probabilities.
  • The width of the beam controls the number of candidate hypotheses considered, and it influences the trade-off between exploration and exploitation.
  • Beam Search often produces better results than Greedy Decoding by exploring a broader range of possibilities.
  • Prefix Beam Search is an extension of Beam Search that considers the possibility of ending the sequence early.
  • It introduces a stopping criterion, allowing the algorithm to consider partial sequences and their likelihood of being completed successfully.
  • This can be particularly useful when the true sequence length is uncertain.

Lexicon-Constrained Decoding

  • Lexicon-Constrained Decoding involves incorporating a lexicon or dictionary of valid words during the decoding process.
  • The decoder explores only those paths that correspond to valid words, reducing the search space and potentially improving accuracy.
  • Word Beam Search is an extension of Beam Search designed specifically for tasks where the output consists of complete words.
  • It incorporates language models and considers word-level probabilities to guide the decoding process.
  • Word Beam Search aims to produce coherent and meaningful output sequences.

Best Path Decoding

  • Best Path Decoding is a simplified decoding strategy where the output sequence is obtained by selecting the most likely path through the model's probability distribution.
  • This is equivalent to Greedy Decoding but may be used when computational resources are limited.

Token Passing

  • Token Passing is an efficient decoding algorithm that uses a set of active tokens to represent possible hypotheses.
  • At each time step, the tokens are updated based on the model's probabilities.
  • Token Passing is particularly useful when dealing with very long sequences as it allows for efficient pruning of unlikely paths.

Conclusion

The choice of decoding algorithm depends on the specific requirements of the task, the nature of the data, and available computational resources. Beam Search is a widely used and effective decoding strategy, but other methods may be more suitable in certain scenarios, such as when a lexicon is available or when dealing with word-level outputs.