In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this domain, this study introduces a novel approach tailored to Automatic License Plate Recognition (ALPR). We developed a robust evaluation framework using a dataset with ground truth coordinates for ALPR. This dataset includes video frames captured under various conditions, including occlusions, to facilitate comprehensive model training, testing, and validation. Our methodology simulates quality degradation using a digital camera image acquisition model, representing how luminous flux is transformed into digital images. The model's performance was evaluated using Video Quality Indicators within an OpenALPR library context. Our findings show that the model achieves a high F-measure score of 0.777, reflecting its effectiveness in assessing video quality for recognition tasks. The proposed model presents a promising avenue for accurate video quality assessment in ALPR tasks, outperforming traditional metrics in typical recognition application scenarios. This underscores the potential of the methodology for broader adoption in video quality analysis for recognition purposes.