Real Time Smart CCTV Untuk Mendeteksi Plat Nomor Kendaraan Menggunakan Optical Character Recognition

Plat nomor kendaraan merupakan salah satu ciri yang membedakan antara satu kendaraan dengan kendaraan lainnya. Plat nomor kendaraan secara resmi dikeluarkan oleh kepolisian wilayah dimana pemilik tinggal. Semakin berkembangnya teknologi, semakin banyak ide yang bermunculan. Salah satunya adalah teknologi deteksi plat nomor kendaraan secara otomatis. Teknologi tersebut telah diterapkan di luar negeri untuk pembayaran jalan tol dan identifikasi pelanggaran lalu lintas. Sistem deteksi plat nomor kendaraan mengambil gambar menggunakan kamera seperti halnya smart CCTV dan menggunakan pengolahan citra untuk mendeteksi dan mengenali karakternya. Dari pengambilan gambar menggunakan IP CCTV tersebut kemudian diolah gambarnya menggunakan pengolahan citra menjadi gambar kembali atau ke bentuk lainnya. Pada tugas akhir ini metode pengolahan citra yang digunakan adalah OCR yaitu mengubah gambar ke dalam bentuk teks. Hasil dari Tugas Akhir ini adalah dapat mendeteksi kotak/letak plat nomor kendaraan dengan akurasi 83,33333333% dan kemudian pembacaan karakter dari plat nomor yang telah terdeteksi yaitu 80% dengan semua karakter benar

IMPROVING THE EFFICIENCY OF TESSERACT OCR ENGINE

This project investigates the principles of optical character recognition used in the Tesseract OCR engine and techniques to improve its efficiency and runtime. Optical character recognition (OCR) method has been used in converting printed text into editable text in various applications over a variety of devices such as Scanners, computers, tablets etc. But now Mobile is taking over the computer in all the domains but OCR still remains one not so conquered field. So programmers need to improve the efficiency of the OCR system to make it run properly on Mobile devices. This paper focuses on improving the Tesseract OCR efficiency for Hindi language to run on Mobile devices as there a not many applications for the same and most of them are either not open source or not for mobile devices. Improving Hindi text extraction will increase Tesseract\u27s performance for Mobile phone apps and in turn will draw developers to contribute towards Hindi OCR . This paper presents a preprocessing technique being applied to the Tesseract Engine to improve the recognition of the characters keeping the runtime low. Hence the system runs smoothly and efficiently on mobile devices(Android) as it does on the bigger machines

Blind area target aiming system and preference selection training system design

A cyber-physical system (CPS) is a system of leveraging computational elements controlling physical entities that is widely applied in our daily life for all kinds of purpose. It helps us build smart devices and make life become much easier. In this report, two projects were designed to show the idea that how cyber-physical system works in human daily life. The first project is designed for personal security, especially for one of the most dangerous job: security service. It helps user defend his back while he/she is in a tough situation while he or she is alone. First there will be a passive infrared sensor working as a threshold and it also helps make sure the target is a human being. Then a web camera will start to work and take pictures of the user’s blind area. A face detection algorithm will be applied to those pictures to locate the position of the target. Finally two servo motors will work together to rotate to a certain degree, pointing the laser pointer to the target’s body to show the warning. A prototype is built to show that the idea works. The second project is focused on the mental stress problem in daily life. Based on the fact that proper light and music can help people get relaxed, a system is designed to help people find out the right choices. The system will be trained to learn a user’s preferences on the brightness and hues of colors, as well as the speed and emotion tone of the music. A commercial product of galvanic skin response sensor is used to indicate the stress level of the user as the response of the training process

Reconocimiento Automático de Matrículas de Automóviles Particulares Mexicanos

En este trabajo se presenta una propuesta para la identificación de matrículas de automóviles mexicanos en las etapas de segmentación e identificación. Las técnicas existentes en el estado del arte para la identificación de matrículas de automóviles son efectivas para matrículas cuyo color de fondo es uniforme y no contienen patrones de textura; además, estas matrículas tienen un alto contraste entre los colores de fondo y el de los caracteres. Las técnicas utilizadas funcionan considerando estos supuestos, pero para el caso de las matrículas mexicanas no siempre reconocen exitosamente las matriculas debido a que estas tienen características diferentes a las de la mayoría de los países. Para abordar este problema se emplea información sobre la norma de fabricación de placas mexicanas, establecida por el gobierno federal. Una de las características que deben cubrir las placas es respecto a las dimensiones de los caracteres, en donde sumando las áreas que ocupan todos los caracteres, la proporción de área que ocupa el conjunto de letras respecto al área de la placa es del 20%. En consecuencia, en una imagen digital, el 20% de los pixeles son ocupados por las letras de la matrícula. Por otra parte, la intensidad de los colores de los caracteres es menor al de los colores del fondo de la placa con el fin de crear alto contraste y así facilitar el reconocimiento de la matrícula. En la etapa de segmentación se utiliza un enfoque similar al propuesto por (Zhang & Zhang, 2003), en el cual para segmentar los caracteres se acentúa la intensidad del 20% de los pixeles con las intensidades más bajas, ya que se asume que estos pixeles corresponden a los caracteres. Los pasos propuestos para el reconocimiento de matrículas son: 1) segmentación de caracteres, 2) reconocimiento de los caracteres. Una vez segmentados los caracteres estos se modelan con descriptores de Fourier y Momentos de Hu. Finalmente en la etapa de identificación se realizaron dos tipos de pruebas con un clasificador bayesiano. La primera tomando todas las características extraídas y la segunda reduciendo la dimensionalidad de los vectores de características usando análisis de componentes principales con el fin de reducir el costo computacional

An algorithm for accuracy enhancement of license plate recognition

This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video. © 2012 Elsevier Inc

An algorithm for accuracy enhancement of license plate recognition

This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video