French Word Recognition Through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM
Optical character recognition (OCR) is a fundamental problem in computer vision. Research studies have shown significant progress in classifying printed characters using deep learning-based methods and topologies. Among current algorithms, recurrent neural networks with long-short term memory blocks called RNN-LSTM have provided the highest performance in terms of accuracy rate. Using the top 5,000 French words collected from the internet including all signs and accents, RNN-LSTM models were trained and tested for several cases. Six fonts were used to generate OCR samples and an additional dataset that included all samples from these six fonts was prepared for training and testing purposes. The trained RNN-LSTM models were tested and achieved the accuracy rates of 99.98798% and 99.91889% for edit distance and sequence error, respectively. An accurate preprocessing followed by height normalization (standardization methods in deep learning) enabled the RNN-LSTM model to be trained in the most efficient way. This machine learning work also revealed the robustness of RNN-LSTM topology to recognize printed characters.
Alex Graves , Santiago Fernández , Jurgen Schmidhuber, "Multi-dimensional Recurrent Neural Networks," in Artificial Neural Networks – ICANN, Porto, 2007.
A. Graves, Supervised sequence labelling with recurrent neural networks, Vol. 385. Heidelberg: Springer, 2012.
Yann LeCun , Léon Bottou, Yoshua Bengio, and Patrick Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber, "A novel connectionist system for unconstrained handwriting recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 31, no. 5, pp. 855-868, 2009.
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber, "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks," in Proceedings of the 23rd international conference on Machine learning, ACM, 2006.
A. Graves, "Practical variational inference for neural networks," in Advances in Neural Information Processing Systems, 2011.
N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
Saman Sarraf, Jian Sun, "ADVANCES IN FUNCTIONAL BRAIN IMAGING: A COMPREHENSIVE SURVEY FOR ENGINEERS AND PHYSICAL SCIENTISTS.," International Journal of Advanced Research, vol. 4, no. 8, pp. 640-660, 2016.
Arindam Chaudhuri , Krupa Mandaviya, Pratixa Badelia, and Soumya K. Ghosh, "Optical Character Recognition Systems for French Language," Optical Character Recognition Systems for Different Languages with Soft Computing, pp. 109-136, 2017.
Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, New Jersey: Pearson Prentice Hall, 2008.
Milan Sonaka, Vaclav, Hlavac, Roger Boyle, Image Processing, Analysis, and Machine Vision, Toronto: Thomson, 2008.
Mozaffari, S. and Soltanizadeh, H., "ICDAR 2009 handwritten Farsi/Arabic character recognition competition," in 2009 10th International Conference on Document Analysis and Recognition, 2009.
Hochreiter, Sepp, and Jürgen Schmidhuber, "Long short-term memory," Neural computation , vol. 9, no. 8, pp. 1735-1780, 1997.
Santiago Fernández, Alex Graves, and Jürgen Schmidhuber, "Phoneme recognition in TIMIT with BLSTM-CTC," arXiv preprint arXiv:0804.3269 , 2008.
- There are currently no refbacks.