Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userImproved the Automatic Number Plate Recognition System (ANPR)
تطوير منظومة التعرف الآلي إلى لوحة السيارة
2878
4 146
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعدّ التعرف الآلي إلى السيارات بواسطة أرقامها المثبتة على لوحتها موضوعاً مهماً, لما لهو من استخدامات متعددة منها التطبيقات الأمنية كمراقبة مداخل مؤسسة حساسة. و مراقبة السيارات على الطرقات و كشف السيارات المسروقة و حتى أنه يمكن أن يفيد في الدراسات الإحصائية كدراسة الازدحام المروري في منطقة ما وغيرها. يعرض هذا العمل تفاصيل بناء نظام تعرف آلي إلى لوحة السيارة و أيضاً التعرف إلى لون السيارة. جرى التركيز في هذا البحث على مرحلة تحويل الأرقام الموجودة داخل صورة لوحة السيارة إلى أرقام فعلية, لتحسين أداء النظام ككل نظرًا لكثرة الأخطاء التي تحدث في هذه المرحلة و بالتالي فشل التعرف بالكامل. قمنا في هذا البحث باستخدام خوارزمية,تحليل المكونات الأساسية (Principal Component Analysis (PCA لللتعرف على أرقام لوحة المفاتيح الموجودة داخل الصورة,و دمجها مع الخوارزمية المستخدمة عادة و هي خوارزمية التعرف البصري Optical Character Recognition (OCR للتقليل من الأخطاء المرتكبة في عملية التعرف على الأرقام , ومن ثم تحسين أداء نظام التعرف الآلي على لوحات السيارات.كما قمنا بإضافة التّعرف إلى لون السيارة (الذي يعدّ موسطاً مميزًا آخر للسيارة)مما يسمح اعتماداً على قاعدة معطيات بكشف السيارات المسروقة و زيادة موثوقية النظام.
The Automatic recognition System to vehicles through its number is an important topic, because of its important uses, such as security applications by monitoring the entrances of a important institutions, monitor the vehicles on the road, detection of stolen cars, and even that could be useful in statistical studies, where we can study the traffic congestion in an area. In this work we offer an overview of the Automatic Number Plate Recognition System (ANPR) through to identify the license plate number, and also recognize the color of car. The focus of this research on the stage of converting the numbers into a picture of a car plate to actual figures, to improve the performance of all system, where many of errors that occur at this stage. In this search was used the algorithm of Principle component analysis (PCA) to identify the numbers plate inside the picture. and its integration with optical character Recognition algorithm(OCR) which usually used for recognition , to minimize errors in recognition numbers and thus improve the performance of the automatic number plate system.and also we add color car recognize(which another important parameter of car) , this helps after return to data base detect stolen vehicles and improve the reliability of system
References used
RUSS- J, 2011 The IMAGE PROCESSING Handbook . Taylor and Francis Group , Sixth Edition,North Carolina State University USA , 853P
GONZALEZ-R, WOODS-R, 2002 Digital Image Processing.Prentice Hall, Second Edition,USA,797p
MARTINSKY-O, 2007ALGORITHMIC AND MATHEMATICAL PRINCIPLES OF AUTOMATIC NUMBER PLATE RECOGNITION SYSTEMS
UKANI-N,MEHTA-H, 2010 An Accurate Method for License Plate Localization using Morphological Operations and Edge Processing , IEEE International Congress on Image and Signal Processing , vol.3. 2488-24
QADRI-M, ASIF-M, 2009 AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM FOR VEHICLE IDENTIFICATION USING OPTICAL CHARACTER RECOGNITION,IEEE International Conference on Education Technology and Computer,vol.5.335-338
rate research
Read More
We explore the application of state-of-the-art NER algorithms to ASR-generated call center transcripts. Previous work in this domain focused on the use of a BiLSTM-CRF model which relied on Flair embeddings; however, such a model is unwieldy in terms
of latency and memory consumption. In a production environment, end users require low-latency models which can be readily integrated into existing pipelines. To that end, we present two different models which can be utilized based on the latency and accuracy requirements of the user. First, we propose a set of models which utilize state-of-the-art Transformer language models (RoBERTa) to develop a high-accuracy NER system trained on custom annotated set of call center transcripts. We then use our best-performing Transformer-based model to label a large number of transcripts, which we use to pretrain a BiLSTM-CRF model and further fine-tune on our annotated dataset. We show that this model, while not as accurate as its Transformer-based counterpart, is highly effective in identifying items which require redaction for privacy law compliance. Further, we propose a new general annotation scheme for NER in the call-center environment.
To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around trans
parency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.
The research presents a design for an automated checking system for students. The
system takes a picture of the student, then it extracts his/her basic facial features. The
network was trained using the reverse spreading algorithm. If a training da
tabase is
generated for each student consisting of 15 training samples contained of the necessary
facial expressions to identify the student for one time at the beginning of the semester, then
the neural network will be trained on students database to obtain a trained neural network
able to identify the students of each category depending on their physical appearance. That
will result in knowing who attends and who does not attend the session. The system
designed for this purpose was supplied with the trained network. The system provides the
possibility of automated checking for students according to the content of the study giving
the alarm in case of the existence of the picture of a student who does not belong to the
same group.
الغاية من هذا البحث بناء نظام لتصنيف نطق الأرقام الانكليزية وذلك بالاعتماد على نماذج ماركوف المخفية في التصنيف وذلك بالاعتماد على طيف الإشارة في استخراج سمات الإشارات
In general, the aim of an automatic speech recognition system is to write down what is said. State of the art continuous speech recognition systems consist of four basic modules: the signal processing, the acoustic modeling, the language modeling and
the search engine. While isolated word recognition systems do not contain language modeling, which is responsible for connecting words together to form understandable sentences.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
