License Plate Recognition (LPR) in video sequences faces challenges such as motion blur, occlusions, and poor lighting. While most research focuses on detection and recognition, frame combination strategies remain mainly underexplored This paper introduces a novel segmentation-free plate recognizer that allows to estimate the confidence of each returned character together with an estimation of the network uncertainty. Evaluated on a challenging dataset of real-world Cuban plates with deformations and adverse conditions, we found combinations that outperforms traditional methods, achieving significant gains in accuracy for low-quality scenarios. These findings highlight the importance of temporal integration in video-based LPR systems.