In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 10⁶ digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs.

Time-delay reservoir computing (RC) systems, particularly those based on semiconductor lasers (SLs), have gained attention due to their low energy consumption, high response rates, and rich nonlinear dynamics. This work investigates the influence of key time parameters—virtual node interval (θ), delay feedback (τ), and data injection period (T) on the performance of SL-based time-delay RC systems. Using the Santa Fe time series prediction task and memory capacity evaluation task, we analyze how these parameters affect prediction accuracy and memory capability. The results reveal that θ = 0.2T_ro (where T_ro is the relaxation oscillation period of the SLs) optimizes prediction performance, while θ = 0.5T_ro maximizes memory capacity. Additionally, feedback delay τ significantly impacts system performance. Shorter τ values (e.g., τ = 0.54T) enhance prediction accuracy, whereas longer τ values (e.g., τ = 1.74T) improve memory capacity. These findings provide valuable insights for optimizing time-delay RC systems, enabling better task-specific performance and stability.