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Neocognitron with improved bend-extractors: Recognition of handwritten digits in the real world

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We have reported previously that the performance of a neocognitron can be improved by a built-in bend-extracting layer. The conventional bend-extracting layer can detect bend points and end points of lines correctly, but not always crossing points of lines. This paper shows that an introduction of a mechanism of disinhibition can make the bend-extracting layer detect not only bend points and end points, but also crossing points of lines correctly. This paper also demonstrates that a neocognitron with this improved bend-extracting layer can recognise handwritten digits in the real world with a recognition rate of about 98%. We use the technique of dual thresholds for feature-extracting S-cells, and higher threshold values are used in the learning than in the recognition phase. We discuss how the threshold values affect the recognition rate.

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  1. Fukushima K. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks 1988; 1: 119–130.

    Google Scholar 

  2. Fukushima K, Miyake S. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 1982; 15: 455–469.

    Google Scholar 

  3. Hubel DH, Wiesel TN. Receptive fields and functional architecture in nonstriate areas (18 and 19) of the cat. J Neurophysiology 1965; 28: 229–289.

    Google Scholar 

  4. Fukushima K, Wake N. Improved neocognitron with bend-detecting cells. Int Joint Conf Neural Networks (IJCNN'92), Baltimore, vol IV. 1992; 190–195.

  5. Fukushima K. Analysis of the process of visual pattern recognition by the neocognitron. Neural Networks 1989; 2: 413–420.

    Google Scholar 

  6. Fukushima K, Tanigawa M. Use of different thresholds in learning and recognition. Neurocomputing 1996; 11: 1–17.

    Google Scholar 

  7. Fukushima K, Nagahara K, Shouno H, Okada M. Training neocognitron to recognise handwritten digits in the real world. World Congress on Neural Networks (WCNN'96), San Diego, 1996; 21–24.

  8. Fukushima K, Nagahara K, Shouno H. Training neocognitron to recognise handwritten digits in the real world. Proc Second Aizu Int Symposium on Parallel Algorithms/Architectures Synthesis (pAs'97), Aizu-Wakamatsu, Japan, 1997; 292–298.

  9. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. Backpropagation applied to handwritten zip code recognition. Neural Computation 1989; 1: 541–551.

    Google Scholar 

  10. Fukushima K, Wake N. Handwritten alphanumeric character recognition by the neocognitron. IEEE Trans Neural Networks 1991; 2: 355–365.

    Google Scholar 

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Fukushima, K., Kimura, E. & Shouno, H. Neocognitron with improved bend-extractors: Recognition of handwritten digits in the real world. Neural Comput & Applic 7, 260–272 (1998).

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