In order to accurately identify and recognize text, it is common practice to employ the Laplacian operation on video images to boost contrast. While Laplacian operation does enhance contrast, it also adds an excessive amount of noise, which is a fact. To alleviate this, the existing approaches provide different filters and enhancement methods. In this paper, we propose a generalized enhancement model based on fractional calculus to increase the quality of images generated by Laplacian procedures.By considering edges and their neighbor information, the recommended method creates a mathematical model for enhancing low contrast information in video and scene photographs. Experimental results on several datasets show that the proposed enhancement model significantly enhances the accuracy of text recognition and identification approaches. When the enhancement model is contrasted with traditional enhancement models, the proposed method outperforms the present models in terms of quality metrics. The effectiveness of the proposed model is validated using text detection and recognition tests.