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

TL;DR

This paper demonstrates that RNN-LSTM models can achieve near-perfect accuracy in recognizing French printed words across multiple fonts, highlighting their robustness and effectiveness in OCR tasks.

Contribution

The study applies RNN-LSTM to French OCR, achieving high accuracy and demonstrating robustness across fonts, with optimized preprocessing for efficient training.

Findings

Achieved over 99.9% accuracy in OCR recognition

Demonstrated robustness of RNN-LSTM across multiple fonts

Validated preprocessing methods for optimal training

Abstract

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.