Towards efficient unconstrained handwriting recognition using Dilated Temporal Convolution Network

Recognition of cursive handwritten images has advanced well with recent recurrent architectures and attention mechanism. Most of the works focus on improving transcription performance in terms of Character Error Rate (CER) and Word Error Rate (WER). Existing models are too slow to train and test networks. Furthermore, recent studies have recommended models be not only efficient in terms of task performance but also environmentally friendly in terms of model carbon footprint. Reviewing the recent state-of-the-art models, it recommends considering model training and retraining time while designing. High training time increases costs not only in terms of resources but also in carbon footprint. This becomes challenging for handwriting recognition model with popular recurrent architectures. It is truly critical since line images usually have a very long width resulting in a longer sequence to decode. In this work, we present a fully convolution based deep network architecture for cursive handwriting recognition from line level images. The architecture is a combination of 2-D convolutions and 1-D dilated non causal convolutions with Connectionist Temporal Classification (CTC) output layer. This offers a high parallelism with a smaller number of parameters. We further demonstrate experiments with various re-scaling factors of the images and how it affects the performance of the proposed model. A data augmentation pipeline is further analyzed while model training. The experiments show our model, has comparable performance on CER and WER measures with recurrent architectures. A comparison is done with state-of-the-art models with different architectures based on Recurrent Neural Networks (RNN) and its variants. The analysis shows training performance and network details of three different dataset of English and French handwriting. This shows our model has fewer parameters and takes less training and testing time, making it suitable for low-resource and environment-friendly deployment. © 2020