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A Neuromorphic Architecture for Context Aware Text Image Recognition

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Abstract

Although existing optical character recognition (OCR) tools can achieve excellent performance in text image detection and pattern recognition, they usually require a clean input image. Most of them do not perform well when the image is partially occluded or smudged. Humans are able to tolerate much worse image quality during reading because the perception errors can be corrected by the knowledge in word and sentence level context. In this paper, we present a brain-inspired information processing framework for context-aware Intelligent Text Recognition (ITR) and its acceleration using memristor based crossbar array. The ITRS has a bottom layer of massive parallel Brain-state-in-a-box (BSB) engines that give fuzzy pattern matching results and an upper layer of statistical inference based error correction. Optimizations on each layer of the framework are introduced to improve system performance. A parallel architecture is presented that incorporates the memristor crossbar array to accelerate the pattern matching. Compared to traditional multicore microprocessor, the accelerator has the potential to provide tremendous area and power savings and more than 8,000 times speedups.

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References

  1. Hecht-Nielsen, R. (2007). Confabulation theory: the mechanism of thought. Springer.

  2. Qiu, Q., Wu, Q., Burns, D., Moore, M., Bishop, M., Pino, R., Linderman, R. (2011). Confabulation based sentence completion for machine reading. Proc. Of IEEE Symposium Series on Computational Intelligence.

  3. Qinru Qiu, Q., Wu, M., Bishop, R. P., Linderman, R. W. (2013). A parallel neuromorphic text recognition system and its implementation on a heterogeneous high performance computing cluster. IEEE Transactions on Computers, 62(5).

  4. Anderson, J. A. (1995). An introduction to neural networks. The MIT Press.

  5. Partzsch, J., & Schuffny, R. (2011). Analyzing the scaling of connectivity in neuromorphic hardware and in models of neural networks. IEEE Transactions on Neural Networks, 22(6), 919–935.

    Article  Google Scholar 

  6. Chua, L. (2011). Resistance switching memories are memristors. Applied Physics A: Materials Science & Processing, 102(4), 765–783.

    Article  MATH  Google Scholar 

  7. Ho, Y. Huang, G. M., Li P. (2009). Nonvolatile memristor memory: device characteristics and design implications. International Conference on Computer-Aided Design (ICCAD), pp.485–490.

  8. Anderson, J., Silverstein, J., Ritz, S., & Jones, R. (1977). Distinctive features, categorical perception, and probability learning: some applications of a neural model. Psychological Review, 84(5), 413.

    Article  Google Scholar 

  9. Yang, Z. R., & Zwolinski, M. (2001). Mutual information theory for adaptive mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 396–403.

    Article  Google Scholar 

  10. Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008). The missing memristor found. Nature, 453, 80–83.

    Article  Google Scholar 

  11. Heittmann A. and Noll, T. G. (2012). Limits of writing multivalued resistances in passive nano-electronic crossbars used in neuromorphic circuits. ACM Great Lakes Symposium on VLSI (GLSVLSI), pp. 227–232.

  12. Ramacher, U., Malsburg, C. V. D. (2010). On the construction of artificial brains. Springer.

  13. Hasegawa, T., Ohno, T., Terabe, K., Tsuruoka, T., Nakayama, T., Gimzewski, J. K., & Aono, M. (2010). Learning abilites achieved by a single solid-state atomic switch. Advanced Materials, 22(16), 1831–1834.

    Article  Google Scholar 

  14. Ahmed, K., Qinru Qiu, P., Malani, M. T. (2014). Accelerating pattern matching in neuromorphic intelligent text recognition system using Intel Xeon Phi Coprocessor. Proc. International Joint Conference on Neural Networks (IJCNN).

  15. Yang, F., Qinru Qiu, M. B., Wu, Q. (2012). Tag-assisted sentence confabulation for intelligent text recognition. Proc. of Computational Intelligence for Security and Defense Applications (CISDA).

  16. Li, Z., Qinru Qiu. (2014). Completion and parsing Chinese sentences using cogent confabulation. Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI).

  17. Wu, Q. Bishop, M., Pino, R., Linderman, R., Qiu, Q. (2011). A multi-answer character recognition method and its implementation on a high- performance computing cluster. 3rd International Conference on Future Computational Technologies and Applications, pp. 7–13.

  18. Kim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2011). A functional hybrid memristor crossbararray/cmos system for data storage and neuromorphic applications. Nano Letters, 12(1), 389–395.

    Article  Google Scholar 

  19. Choi, B. J., Chen, A. B., Yang, X., & Chen, I.-W. (2011). Purely electronic switching with high uniformity, resistance tunability, and good retention in pt-dispersed sio2 thin films for reram. Advanced Materials, 23(33), 3847–3852.

    Google Scholar 

  20. Zweig, G., Burges. C. J. C. (2012). A challenge set for advancing language modeling. In Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, pages 29–36. Association for Computational Linguistics.

  21. Li, B., Zhou, E., Huang, B., Duan, J., Wang, Y., Xu, N., Zhang J., Yang, H. (2014). Large scale recurrent neural network on GPU. Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 4062–4069.

  22. Voorhies, R.C., Elazary, L., Itti, L. (2012). Neuromorphic Bayesian surprise for far-range event detection, on advanced video and signal-based surveillance (AVSS), IEEE Ninth International Conference on, pp 1–6, 18–21 Sept. 2012.

  23. Neftci, E., Das, S., Pedroni, B., Kreutz-Delgado, K., & Cauwenberghs, G. (2014). Event-driven contrastive divergence for spiking neuromorphic systems. Frontiers in Neuroscience, 7, 272.

    Article  Google Scholar 

  24. Schmuker, M., Pfeil, T., & Nawrot, M. P. (2014). A neuromorphic network for generic multivariate data classification. Proceedings of the National Academy of Sciences, 111(6), 2081–2086.

    Article  Google Scholar 

  25. Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., & Modha, D. S. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668–673.

    Article  Google Scholar 

  26. Yakopcic, C., Hasan, R., Taha, T. M. (2014). Tolerance to defective memristors in a neuromorphic learning circuit. Aerospace and Electronics Conference, NAECON 2014-IEEE National, (pp. 243–249). IEEE.

  27. Bichler, O., Suri, M., Querlioz, D., Vuillaume, D., DeSalvo, B., & Gamrat, C. (2012). Visual pattern extraction using energy-efficient “2-PCM synapse” neuromorphic architecture. IEEE Transactions on Electron Devices, 59(8), 2206–2214.

    Article  Google Scholar 

  28. Gustavsson, M., Wikner, J. J., & Tan, N. (2000). CMOS data converters for communications. Springer Science & Business Media.

  29. Liu, X., Mao, M., Liu, B., Li, H., Chen, Y., Li, B., Wang, Y. (2015). RENO: A High-efficient Reconfigurable Neuromorphic Computing Accelerator Design, Proc. of Design Automation Conference.

  30. Zhirnov, V. V., Meade, R., Cavin, R. K., & Sandhu, G. (2011). Scaling limits of resistive memories. Nanotechnology, 22, 25.

    Article  Google Scholar 

  31. Pi, S., Lin, P., & Xia, Q. (2013). Cross point arrays of 8 nm 38 nm memristive devices fabricated with nanoimprint lithography. Journal of Vacuum Science and Technology, B31, 06FA02.

    Article  Google Scholar 

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Correspondence to Zhe Li.

Additional information

This work was partially supported by the National Science Foundation under Grants CCF-1337198 and CCF-1337300.

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Qiu, Q., Li, Z., Ahmed, K. et al. A Neuromorphic Architecture for Context Aware Text Image Recognition. J Sign Process Syst 84, 355–369 (2016). https://doi.org/10.1007/s11265-015-1067-4

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  • DOI: https://doi.org/10.1007/s11265-015-1067-4

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