IndexAbstractIntroductionExisting SystemProposed SystemConclusionAbstractIn the modern world, there is a huge increase in traffic congestion and the rate at which people purchase vehicles increases significantly every year. Traffic signals are used to control the flow of traffic and are the most essential component of road safety and traffic control. The main objective of this paper is to propose a solution for automatic control of traffic signs based on traffic density. This will be done by analyzing traffic density through an infrared sensor. A threshold value will be set above which the traffic density will be considered "high" and below which it will be considered "low". Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay A maximum value above which a signal cannot remain open will also be set. When a particular road has a high traffic density, it will have more time in the green signal than roads that have a lower traffic density. Once you have chosen the desired operation, it is sent directly to the traffic light, which modifies the timer and lights up accordingly. Coding for image processing is done in MATLAB and the main concepts used are image processing, image cropping and conversion. Introduction The concept of text recognition has existed since the beginning of the 20th century. Text recognition is commonly known as OCR (optical character recognition). The first optical character recognition can be traced back to 1914. These devices were mainly used to help the blind. With the passage of time and great advancements in the field of technology, these devices have also seen tremendous improvements. The devices can now be used to translate printed text into various languages. The system proposed in this article recognizes handwritten text and converts it into a printed format which can then be displayed on the screen. Despite various advances in the field of character recognition, studies involving the conversion of handwritten text have been quite rare. This is mainly due to the fact that, unlike the case of converting printed text, handwriting varies from person to person and therefore the software will have to individually identify and recognize each person's handwritten character. Since the characters to be converted are handwritten texts, it is practically impossible to create a database that contains handwritten characters because, as mentioned before, handwriting varies from person to person. The system proposed in the article uses the convex shape hull algorithm to identify and convert handwritten text. This method is extremely effective as it significantly reduces the computation time and can also recognize each person's handwritten text individually. The proposed system uses Convex Hull Matching (CHM) to differentiate each letter individually. The article also highlights the various steps used when recognizing and converting handwritten text into a printed text format. The development of the existing system and progress in various aspects of extracting text information from an image dates back to the 20th century. These developments were used for specific applications such as extraction from printed pages. Although extensive research has been conducted, it is not easy to design a general purpose series system. This is because there are many possible sources of variations and outcomes whenextracts the text from the source. Content shadowed by textured background or complex, low-contrast images or images with variations in font size, style, color, orientation, and alignment. These variations make the problem very difficult, which in turn makes it very difficult to draw automatically. Commonly used text detection methods can be classified into three categories. The first category consists of related component-based methods, which assume that text areas have uniform colors and satisfy certain size, shape, and spatial alignment constraints. These methods are not effective when the text has similar colors to the background, so this would most likely result in improper detection. The second category is texture-based methods, which assume that text areas have a special texture. All of these methods are less sensitive to background colors, so they may not be able to distinguish between text and text-like backgrounds. The third category is edge-based methods. In this case, text areas are detected assuming that the edge of the background and object areas are sparser than those of the text areas. These kinds of approaches are not very effective and suitable for detecting large font text. Compared with the method based on Support Vector Machines (SVM) with text-based multilayer perceptions (MLP), the verification on four independent features including distance map feature, grayscale spatial derivative feature, variance feature of the constant gradient and the functionality of the DCT coefficients. Better detection results are achieved by using SVM instead of MLP. Multi-resolution based text detection methods are often adopted to detect texts in different scales. Texts with different scales will have different values. So, in this article we present an effective and alternative way to recognize handwritten text. Proposed System The system we proposed in this paper is an advanced version of the existing system, with improved text detection and recognition capabilities. The proposed structural improvements are: -Text Detection: This stage takes the image input and decides whether it contains text or not. It also identifies text regions in the image using the convex hull method. Text localization: Text localization merges text areas to formulate the text content and define the boundary around the text content. Text Binarization: This step is used to segment the text content from the background into the delimited text contents. By converting the given image to a grayscale image, the binary value is determined. The output of text binarization is the binary image, where text pixels and background pixels appear in two different binary layers. Character Recognition: The final module of the text extraction process is character recognition. This module converts the binary text object to the convex hull image for which a value is determined. The purpose of optical character recognition is to classify optical patterns for handwritten text corresponding to alphanumeric characters or other characters. This is done using the OCR process which involves several steps such as segmentation, feature extraction and classification. In principle, you can now use any standard OCR software to recognize text in segmented panes. A careful look at the properties of candidate character regions in the segmented frames or image reveals that most of the