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Week 4

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In Week 4, we faced major challenges in Optical Character Recognition (OCR) for license plates. The initial OCR strategy delivered poor results due to multiple factors, including motion blur, occlusion, perspective distortion, and character misalignment. To overcome these issues, we implemented license plate character correction, refined segmentation techniques, and integrated more advanced text detection models. Eventually, we selected the CRAFT model for robust text detection and further optimized our pipeline to ensure high accuracy. Additionally, we extended our work to video processing to enable real-time application. Challenges in OCR and Initial Solutions 1. Factors Affecting OCR Performance Our initial OCR implementation struggled due to: Blurriness and Occlusion : Vehicles at varying distances caused blurred license plate images, while partial occlusions further degraded recognition accuracy. Perspective Distortion : Different camera angles led to skewed and curved ...
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Week 2

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 In week 2, we finally get the equipment(Raspberry PI, LCD screen. camera, SD card) for our project, but we encountered multiple challenges, including SD card burning failures and difficulties accessing the Raspberry Pi system desktop. At the same time, we began transitioning from traditional computer vision methods to deep learning for improved license plate recognition accuracy. Lab Day Using Raspberry PI pin map and LCD pin layout as guide, we built the circuit. Challenges and Issues Faced SD Card Burning Failure One of the major obstacles was the failure to burn the operating system onto the SD card. Several attempts were made using different SD cards and flashing software such as Balena Etcher and Raspberry Pi Imager. The common issues encountered included: SD card not being recognized by the system. Flashing process getting stuck midway. Errors upon booting the Raspberry Pi after flashing. To address these issues, we tested multiple SD cards, reformatted them using different ...
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Week 1

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Allocation of Roles and Analysis: During the initial week of our project, our team held a discussion to delegate specific roles and responsibilities to each member. After that, we defined the project requirements and outlined the software architecture. Lab Day: Today, our original task was to solve the hardware problem, but when we arrived at the lab, we found that we had no materials for our group, so we gave priority to the research and discussion of the software problem. The components we needed included: So we shifted our focus this week to software. We successfully implemented the core functionality of the recognition algorithm and completed the coding phase. However, during testing, we encountered some problems. In the first week of our project, we focused on developing a license plate recognition system using traditional computer vision techniques. Our initial approach relied on OpenCV, a widely used library for image processing. Instead of utilizing deep learning models, we opt...
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