Human–computer interaction is demanded for natural and convenient approaches, in which finger-writing recognition has aroused more and more attention. In this paper, a device-free finger-writing character recognition system based on an array of time-of-flight (ToF) distance sensors is presented. The ToF sensors acquire distance values between sensors to a writing finger within a 9.5 × 15 cm square on a surface at specific time intervals and send distance data to a low-power microcontroller STM32F401, equipped with deep learning algorithms for real-time inference and recognition tasks. The proposed method enables one to distinguish 26 English lower-case letters by users writing with their fingers and does not require one to wear additional devices. All data used in this work were collected from 21 subjects (12 males and 9 females) to evaluate the proposed system in a real scenario. In this work, the performance of different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNNs) and bidirectional LSTM (BiLSTM), was evaluated. Thus, these algorithms provide high accuracy, where the best result is extracted from the LSTM, with 98.31% accuracy and 50 ms of maximum latency.