Writer Adaptation for E2E Arabic Online Handwriting Recognition Via Adversarial Multi Task Learning

The importance of online handwriting recognition has been rapidly increasing over recent years due to the rapid technological advances in handheld devices and communication software with handwriting interfaces. Deep learning end-to-end (E2E) models have provided high recognition rates as part of online handwriting recognition systems. However, attaining even higher performance levels requires supplementing these models with adaptation techniques that cater to individual penmanship. This study proposes a writer adaptation technique for Arabic online handwriting recognition systems that employs adversarial Multi-Task Learning (MTL). Adversarial training and MTL modify the deep-features distribution of the Writer Dependent (WD) model, leading its output to closely resemble that of the Writer Independent (WI) model. The design of the proposed method entails two tasks: label classification (primary task) and model features discrimination (secondary task). Our method was designed to jointly optimize both sub-networks. The proposed technique was tested against the E2E Connectionist Temporal Classification (CTC) based model, a combination of both Convolutional Neural Networks (CNNs) and Bidirectional Long Short-term Memory (BiLSTM). The proposed models were trained and evaluated against two large datasets (the Online-KHATT and CHAW). In supervised adaptation, it achieved an absolute Character Error Rate (CER) of up to 1.83% and an absolute Word Error Rate (WER) reduction of 11.71% over the WI model. Additionally, supervised adaptation achieved an absolute CER of up to 0.84% and an absolute WER reduction of 6.77% over the fine-tuned model. In unsupervised adaptation, the proposed method achieved an absolute CER of up to 0.5% absolute and an absolute WER reduction of 1.74% absolute (WER) reduction over the WI. Our experimental results indicate that our proposed supervised writer adaptation can achieve significant improvements in recognition accuracy compared with the baseline models: WI and fine-tuned models.