Handwriting recognition system provides people a convenient and alternative way for writing in the air with fingers rather than typing keyboards. For people with blurred vision and patients with generalized hand neurological disease, writing in the air is particularly attracting due to the small input screen of smartphones and smartwatches. Existing recognition systems still face drawbacks such as requiring to wear dedicated devices, relatively low accuracy and infeasible for cross domain identification, which greatly limit the usability of these systems. To address these issues, we propose WiWrite, an accurate device-free handwriting recognition system which allows writing in the air without a need of attaching any device to the user. Specifically, we use Commercial Off-The-Shelf (COTS) WiFi hardware to achieve fine-grained finger tracking. We develop a CSI division scheme to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces the noise of the CSI amplitude. To automatically retain low noise data for identification, we propose a self-paced dense convolutional network (SPDCN), which consists of the self-paced loss function based on a modified convolutional neural network, together with a dense convolutional network. Comprehensive experiments are conducted to show the merits of WiWrite, revealing that, the recognition accuracies for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve a one-fit-for-all recognition regardless of environment diversities.