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Regular expressions for decoding of neural network outputs.

Tobias Strauß1, Gundram Leifert1, Tobias Grüning1

  • 1Department of Mathematics, University of Rostock, Rostock, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|April 15, 2016
PubMed
Summary

This study introduces an efficient decoder for neural networks using Connectionist Temporal Classification (CTC) in handwriting recognition. Regular expressions and finite automata enable fast, accurate text decoding, benefiting various applications.

Keywords:
Connectionist Temporal ClassificationDecodingHandwriting recognitionRegular expressions

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neural networks trained with Connectionist Temporal Classification (CTC) are effective for tasks like handwritten text recognition.
  • Decoding the output of CTC models can be computationally intensive, limiting real-world applications.

Purpose of the Study:

  • To develop a computationally efficient decoder for CTC-trained neural networks.
  • To leverage regular expressions and finite automata for improved handwritten text recognition.

Main Methods:

  • Utilized regular expressions to model expected writing structures.
  • Constructed a decoder based on the corresponding finite automata.
  • Performed theoretical analysis to identify and eliminate redundant calculations.
  • Introduced an approximation technique to significantly accelerate the decoding process.

Main Results:

  • The proposed decoder demonstrates high efficiency compared to existing methods.
  • An approximation strategy yields substantial speed-up in decoding.
  • The approximation's failure (when regex doesn't match ground truth) is often inconsequential due to probability underestimation.

Conclusions:

  • The developed decoder offers a practical and efficient solution for CTC-based handwritten text recognition.
  • The method's efficiency and accuracy make it suitable for diverse applications, including information retrieval and full text recognition.