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Optical character recognition (OCR) is a fundamental problem in computervision. Research studies have shown significant progress in classifying printedcharacters using deep learning-based methods and topologies. Among currentalgorithms, recurrent neural networks with long-short term memory blocks calledRNN-LSTM have provided the highest performance in terms of accuracy rate. Usingthe top 5,000 French words collected from the internet including all signs andaccents, RNN-LSTM models were trained and tested for several cases. Six fontswere used to generate OCR samples and an additional dataset that included allsamples from these six fonts was prepared for training and testing purposes.The trained RNN-LSTM models were tested and achieved the accuracy rates of99.98798% and 99.91889% for edit distance and sequence error, respectively. Anaccurate preprocessing followed by height normalization (standardizationmethods in deep learning) enabled the RNN-LSTM model to be trained in the mostefficient way. This machine learning work also revealed the robustness ofRNN-LSTM topology to recognize printed characters.

French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM