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CA-NN: a cellular automata neural network for handwritten pattern recognition

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Abstract

Convolutional neural networks (CNNs) are best suited for image data. The most important layer in CNNs is the convolution layer. In this paper, cellular automata neural network or CA-NN is proposed for handwritten pattern recognition that replaces the convolution layer in CNN with the cellular automata (CA) layer. The idea is to make CNNs more biological where an image pattern would grow or decay instead of convolving. To grow or decay an image, CA is used as they are designed precisely for this purpose. In doing so, what would be the response of CA-NN to smaller data sets since CNNs require very large data to train. The model is tested and compared with 3 different well-known CNN architectures using 6 relatively small-sized handwritten data sets. The experimental results are very promising and raise very interesting future directions.

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References

  • Abdelazeem S, El-Sherif E (2008) Arabic handwritten digit recognition, Int J Doc Anal Recognit (IJDAR) 11(3):127–141. http://datacenter.aucegypt.edu/shazeem/

  • Ahamed P, Kundu S, Khan T, Bhateja V, Sarkar R, Mollah AF (2020) Handwritten arabic numerals recognition using convolutional neural network. J Ambient Intell Human Comput

  • Ahranjany SS, Razzazi F, Ghassemian MH (2010) A very high accuracy handwritten character recognition system for farsi/arabic digits using convolutional neural networks. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA), IEEE, pp 1585–1592

  • Alom MZ, Sidike P, Taha TM, Asari VK (2017) Handwritten bangla digit recognition using deep learning. arXiv preprint http://arxiv.org/abs/1705.02680

  • Altwaijry N, Al-Turaiki I (2020) Arabic handwriting recognition system using convolutional neural network. Neural Comput Appl, pp 1–13

  • Bell DI (1994) High life–an interesting variant of life. Available from David Bell’s

  • Bhattacharya U, Chaudhuri BB (2009) Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Trans Pattern Anal Mach Intell 31(3):444–457

    Article  Google Scholar 

  • Bhattacharya U, Chaudhuri B (2005) Databases for research on recognition of handwritten characters of indian scripts. In: Eighth international conference on document analysis and recognition (ICDAR’05), pp 789–793

  • Boufenar C, Kerboua A, Batouche M (2018) Investigation on deep learning for off-line handwritten Arabic character recognition. Cognit Syst Res 50:180–195

    Article  Google Scholar 

  • Cireşan D, Meier U, Multi-column deep neural networks for offline handwritten chinese character classification. In: (2015) International joint conference on neural networks (IJCNN). IEEE 2015:1–6

  • Conway J (1970) The game of life. Sci Am 223(4):4

    Google Scholar 

  • Fornés A, Lladós J, Sánchez G (2007) Old handwritten musical symbol classification by a dynamic time warping based method. In: International workshop on graphics recognition, Springer, pp 51–60

  • Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, Vol. 1, MIT press Cambridge, Cambridge

  • Hazra A, Choudhary P, Inunganbi S, Adhikari M (2020) Bangla-meitei mayek scripts handwritten character recognition using convolutional neural network. Appl Intell, pp 1–21

  • Khodabandelou G, Routhier E, Mozziconacci J (2020) Genome annotation across species using deep convolutional neural networks. Peer J Comput Sci 6:e278

    Article  Google Scholar 

  • LeCun Y, Cortes C, Burges CJ (1998) The mnist database of handwritten digits

  • Mehrotra K, Jetley S, Deshmukh A, Belhe S (2013) Unconstrained handwritten devanagari character recognition using convolutional neural networks. In: Proceedings of the 4th international workshop on multilingual OCR, ACM, p 15

  • Parseh M, Rahmanimanesh M, Keshavarzi P (2020) Persian handwritten digit recognition using combination of convolutional neural network and support vector machine methods. Int Arab J Inf Technol 17(4):572–578

    Google Scholar 

  • Tsai C (2016) Recognizing handwritten Japanese characters using deep convolutional neural networks. university of Stanford in Stanford, California

  • Wali A, Saeed M (2018) Biologically inspired cellular automata learning and prediction model for handwritten pattern recognition. Biol Insp Cognit Arch 24:77–86

    Google Scholar 

  • Wali A, Saeed M (2019) m-calp: Yet another way of generating handwritten data through evolution for pattern recognition. Biosystems 175:24–29

    Article  Google Scholar 

  • Wolfram S (2002) A new kind of science, Wolfram media

  • Yang W, Jin L, Tao D, Xie Z, Feng Z (2016) Dropsample: A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten chinese character recognition. Pattern Recogn 58:190–203

    Article  Google Scholar 

  • Yuan A, Bai G, Jiao L, Liu Y (2012) Offline handwritten english character recognition based on convolutional neural network. In: (2012) 10th IAPR international workshop on document analysis systems. IEEE. pp 125–129

  • Zeng T, Li R, Mukkamala R, Ye J, Ji S (2015) Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. BMC Bioinf 16(1):147

    Article  Google Scholar 

  • Zhou J, Peng H (2007) Automatic recognition and annotation of gene expression patterns of fly embryos. Bioinformatics 23(5):589–596

    Article  Google Scholar 

Download references

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  1. Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan

    Aamir Wali

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  1. Aamir Wali
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Wali, A. CA-NN: a cellular automata neural network for handwritten pattern recognition. Nat Comput (2022). https://doi.org/10.1007/s11047-022-09937-8

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