High Performance Text Recognition Using a Hybrid Convolutional-LSTM Implementation

Optical character recognition (OCR) has made great progress in recent years due to the introduction of recognition engines based on recurrent neural networks, in particular the LSTM architecture. This paper describes a new, open-source line recognizer combining deep convolutional networks and LSTMs, implemented in PyTorch and using CUDA kernels for speed. Experimental results are given comparing the performance of different combinations of geometric normalization, 1D LSTM, deep convolutional networks, and 2D LSTM networks. An important result is that while deep hybrid networks without geometric text line normalization outperform 1D LSTM networks with geometric normalization, deep hybrid networks with geometric text line normalization still outperform all other networks. The best networks achieve a throughput of more than 100 lines per second and test set error rates on UW3 of 0.25%.