Table of ContentsAbstractIntroductionExisting SystemProposed SystemConclusionAbstractIn the modern world, traffic congestion is increasing significantly and the rate at which people purchase vehicles is increasing significantly every year. Traffic lights are used to control the flow of traffic and are the most essential element of road safety and traffic control. The main objective of this article is to propose a solution for automatic control of traffic lights based on traffic density. This will be done by analyzing traffic density using an infrared sensor. A threshold value will be set above which traffic density will be considered “high” and below which will be considered “low”. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get the original essay A maximum value will also be defined, beyond which a signal cannot remain open. When a particular road has a high traffic density, it will spend more time at the green signal than roads with lower traffic density. Once the desired operation is chosen, it is sent directly to the traffic light, which changes the timer and lighting accordingly. The coding of image processing is done in MATLAB and the main concepts used are image processing, cropping and image conversion.IntroductionThe concept of text recognition has existed since the beginning of the 20th century. Text recognition is commonly called OCR (Optical Character Recognition). The first optical character recognition devices date 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 considerable improvements. The devices can now be used to translate printed text into different languages. The system proposed in this paper recognizes handwritten text and converts it into a printed format which can then be viewed on screen. Despite the various advances in the field of character recognition, studies involving the conversion of handwritten texts have been quite rare. This is mainly because, unlike converting printed text, handwriting varies from person to person and therefore the software will need to identify and recognize each person's handwriting individually. Since the characters to be converted are handwritten texts, it is practically impossible to create a database containing the handwritten characters because, as mentioned earlier, handwriting varies from person to person. The system proposed in the article uses the convex character. shell algorithm to identify and convert handwritten text. This method is extremely efficient because it significantly reduces the calculation time and also allows each person's handwritten text to be recognized individually. The proposed system uses Convex Hull Matching (CHM) to differentiate each letter individually. The article also highlights the different steps used when recognizing and converting handwritten text into a printed text format. The development and progress of the existing system in various aspects of extracting textual information from an image has existed since the 20th century. These developments were used for specific applications such as extraction from printed pages. Although extensive research has been carried out, however, it is not easy to design a general-purpose serial system. In fact, heThere are many possible sources of variation and results when extracting the text from the source. Shaded content from 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 makes automatic drawing very difficult. Commonly used text detection methods can be classified into three categories. The first category is connected component-based methods, which assume that text regions 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, which would likely result in incorrect detection. The second category includes texture-based methods, which assume that text boxes have a special texture. All of these methods are less sensitive to background colors, so they may not be able to differentiate between texts and text-like backgrounds. The third category includes edge-based methods. In this case, text regions are detected by assuming that the background edge and object regions are sparser than those of text regions. This type of approach is not very efficient and is suitable for detecting texts with large font sizes. Compared to the method based on support vector machines (SVM) with text-based multi-layer perceptions (MLP), verification on four independent features which includes distance map function, spatial derivative function in levels of gray, the constant gradient variance function and the DCT coefficient function. Better detection results are obtained using SVM rather than MLP. Text detection methods based on multiple resolutions are often adopted to detect texts at different scales. Texts with different scales will have differences. Thus, 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 better text detection and recognition capabilities. The proposed structural improvements are: - Text detection: this phase takes input from an image 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 regions to formulate text content and define the boundary around text content. Text Binarization: This step is used to segment the background text content into the delimited text content. By converting the given image to a grayscale image, the binary value is determined. The result of text binarization is the binary image, where the text pixels and the background pixels appear in two different binary levels. Character recognition: The final module of the text mining process is character recognition. This module converts the binary text object into a convex hull image for which a value is determined. The purpose of optical character recognition is to classify optical patterns of handwritten text corresponding to alphanumeric or other characters. This is done using the OCR process which involves several steps such as segmentation, feature extraction and classification. In principle, any standard OCR software can now be used to