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In this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms.

Image enhancement methods are known among the most important image processing techniques. Here, image enhancement is considered as an optimization problem and a new heuristic optimization algorithm namely the Black Hole is used to solve it. Image enhancement is a nonlinear optimization problem with its particular constraints and the enhancement process will be done by intensifying each pixel's content. In this paper, BH is employed to find the image's optimum parameters of the transfer function in order to get the best results. BH is used here for its simplicity, ease of implementation, and also its invincibility against the parameter tuning issues. Performance of the proposed enhancement algorithm is tested against some of the well-known enhancement techniques viz. GA, PSO, HE and CS, and the obtained results indicate the robustness and also the outperformance of the proposed algorithm among its other counterparts. Enhancement in opaque images consisting of immense dominant gray values can be listed as one of the proposed method's superiority to that of the other available in literature, which will turn the input image into an enhanced image, featuring embossed textures.

A new Persian license plate recognition algorithm is presented. These operations are highly susceptible to error, especially where the image consists of large amount of either vehicle's linked components or the other existing objects. Although the proposed character recognition procedure is highly optimized for Persian plates, the localization parts can be employed for all types of vehicles. Minimum rectangle bounding box is replaced the common bounding box methods, compensating normal bounding box's inherent flaws. License plate possibility ratio (LPPR) is a robust method proposed here to localize the plate. New method of finding plate's location out of so many rectangles, considering "Sensitive to angle" criterions for characters has also been presented. It should be noted that the process is regardless of the plate's location. Different approach on thresholding namely: "Dynamic Thresholding" is used to overcome the probable drawbacks caused by inappropriate lighting. From OCR point of view, a graph, consisting of two specifications will be formed and a set of rules will be defined to capture the character's label. An automated harassment section is added as the denoising filter, in order to omit the grinning ramifications. Presenting the best percent accuracy (95.33%) among relevant well-known algorithms in localization procedure with 25ms run time of the program, and also the outstanding results with over 97% of percent accuracy in character recognition of Persian plates with 30ms run time of the program on Linux and also average of 90ms on Android, can be listed as strong proofs of algorithm's efficiency.

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