Presently image segmentation remains the most crucial stage in the image processing system. The main idea ofimage segmentation is to partition or divide a random image into several partitions depending on the problem to solve. In this paper, we will be presenting a new method of skin cancer detection based on Otsu’s thresholding algorithm and markercontrolled watershed method. This hybridization process is first of all started by segmenting the input image using fuzzy c-means algorithm which is a clustering method that gives the possibility to a pixel to belong to one or more clusters. After that, we will apply multi-Otsu which is a thresholding algorithm that separates the pixels of an image into a variety of classes depending on the intensity of the gray levels. The next step of this proposed method is the marker-controlled watershedalgorithm that divides the image into homogenous areas or regions by using edge-detection concepts including mathematical morphology. The proposed technique was applied and experienced using several images of different types of skin cancer that were collected and gathered from the web and also from the Kaggle dataset. To assess the worth of the achieved results, we used several evaluation metrics like dice coefficient, sensitivity, specificity as well as Jaccard similarity that all have shown good and satisfactory results.

Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the detection system, it was manually labeled. Afterward, we improved the recognized license plate using machine learning methods. To do this, we used kernel methods as well as thresholding to get rid of the extra vertical lines on the plate. After that, we employed Arabic OCR along with Easy OCR methods to decipher the Latin and Arabic characters on the number plate. Eventually, the proposed method achieved an F1 score of 98%,with a precision and recall of 97% and 98%, respectively. We also obtained an accuracy of 99% for image segmentation. The segmentation and detection results from the suggested strategy have shown satisfactory results.

License plate detection has always been challenging, especially with the increasing number of smart radars on roads and highways. By 2021, over 1 billion smart traffic radars have been provided worldwide for the gendarmerie as well as for road control officers from the National Security. This paper introduces a novel Arabic and Latin license plate detection and recognition method. In the first stage of the proposed method and after gathering images to build a new dataset, we used YOLOv7 to detect and localize the license plate in the image. The dataset has been labeled manually before feeding it to the detection system. Second, we applied some of the machine learning algorithms to enhance the detected license plate. For this, we employed thresholding as well as the kernel algorithms in order to remove the additional vertical lines in the plate. Afterward, we used the Arabic OCR as well as Easy OCR techniques to recognize the Arabic and the Latin letters on the license plate. The proposed detection algorithm achieved a precision of 97% and a recall of 98%, consequently, we achieved an F1 score of 98% in license plate detection. For image segmentation, while using Arabic OCR and Easy OCR to segment and extract characters from the detected license plate, we achieved an accuracy of 99 %. The proposed method gave satisfactory results for both detection and segmentation.