Character Time-series Matching For Robust License Plate Recognition

Automatic License Plate Recognition (ALPR) is becoming a popular study area and is applied in many fields such as transportation or smart city. However, there are still several limitations when applying many current methods to practical problems due to the variation in real-world situations such as light changes, unclear License Plate (LP) characters, and image quality. Almost recent ALPR algorithms process on a single frame, which reduces accuracy in case of worse image quality. This paper presents methods to improve license plate recognition accuracy by tracking the license plate in multiple frames. First, the Adaptive License Plate Rotation algorithm is applied to correctly align the detected license plate. Second, we propose a method called Character Time-series Matching to recognize license plate characters from many consequence frames. The proposed method archives high performance in the UFPR-ALPR dataset which is \boldmath$96.7\%$ accuracy in real-time on RTX A5000 GPU card. We also deploy the algorithm for the Vietnamese ALPR system. The accuracy for license plate detection and character recognition are 0.881 and 0.979 $mAP^{test}$@.5 respectively. The source code is available at https://github.com/chequanghuy/Character-Time-series-Matching.gi

Comparasi Edge Detection Roberts dan Morfologi pada Deteksi Plat Nomor Kendaraan Roda Dua

Refers to the difficulty factor on the detection plate on this research will focus on the detection of two-wheeled vehicle number plate, where the data will be taken from the farm garage. The resolution of this problem will be attempted using techniques of digital image processing method of detection of edge (edge detection) and morphology. Study on the evaluation and the research results will be calculated using the technique of confussion matrix, this technique will measure on the success rate of approach will be obtained from the proposed method. of the proposed method is sufficiently high, namely 53% success rate, while the value of positive predictive value by which this value to know the success rate of the method to detect the entire image test is still extremely less just reached 40% success, while the value of negative value which is the value of preditive to know separation detection noise his success pretty well with a value of 56% success rat

Identifikasi Label Kode Leadframe pada Sistem Uji Fungsi RFID Chip Berbasis Template Matching

The label code in a production process has an important function as an identifier. Making label code on leadframe can be done by laser engraving or painting method. The RFID chip is moved to the leadframe media to carry the signal from the die level to the outside circuit. The conductivity function test is carried out to ensure the circuit works after the die attach and wire bond processes from the RFID chip. Based on the case study on TFME, the results of this function test are stored with manual labeling. The template matching method is applied to read the leadframe label code which will then be scanned in the form of a test technical report. This label identification system uses a Raspberry Pi 3 equipped with a Sony IMX219V2 camera with a resolution of 8 MP. 2 characters that are similar, namely numbers 2 and Z, are eliminated from the sample. In addition, the label code is made in characters with vertical and horizontal lines. In this result, a success rate of 100%. This identified label code then becomes the leadframe identification number in the form of a report file that is stored in a document format.Kode label pada sebuah proses produksi memiliki fungsi penting untuk pengidentifikasi (identifier). Pembuatan kode label pada leadframe dapat dilakukan dengan metode laser engraving ataupun painting. Chip RFID dipindahkan pada media leadframe untuk membawa sinyal dari level die menuju rangkaian diluar. Uji fungsi konduktifitas dilakukan untuk memastikan bekerjanya rangkaian setelah proses die attach dan wire bond dari chip RFID. Berdasarkan studi kasus pada TFME, hasil uji fungsi ini disimpan dengan pemberian label yang masih manual. Metode template matching diterapkan untuk membaca kode label leadframe yang kemudian akan dipindai dalam bentuk laporan teknis pengujian. Sistem identifikasi label ini dengan Raspberry Pi 3 dan dilengkapi kamera Sony IMX219V2 dengan resolusi 8 MP.  Dua karakter yang mirip yakni angka 2 dan Z dieliminasi dari sample. Selain itu kode label dibuat dalam karakter dengan garis vertikal dan horizontal. Hasil pengujian menunjukkan tingkat keberhasilan pembacaan sebesar 100%. Kode label yang telah teridentifikasi ini selanjutnya menjadi nomer identifikasi leadframe dalam bentuk report file yang disimpan dalam format dokumen

Automatic License Plate Recognition System using Histogram Graph Algorithm

Character recognition is one of the most important applications of image processing. In last couple of decades, the numbers of vehicles have been increasing day by day. With this increase, it is becoming difficult to keep track of each vehicle for the purpose of law enforcement and traffic management. Thus, we need that type of system, which is capable of providing appropriate solutions to the traffic issues and hence this Automatic License Plate Recognition System is developed using Histogram graph algorithm in MATLAB. In which image is captured from camera and converted into Gray scale image for preprocessing .After conversion, dilation process is applied on image. After dilation horizontal and vertical edge processing has been done and passed these histograms through low pass filter. After filtration, image is segmented and license plate is extracted. For detection each character separately from detected license plate smearing algorithm is used

SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE

The purpose of this paper is to design and implement an automatic number plate recognition system. The system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and automatically reads it with no prior assumption of background made. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading. The string output is then used to query a relational database to obtain the desired user data. This particular paper utilizes the intersection of a hat filtered image and a texture mask as the means of locating the number plate within the image. The accuracy of location of the number plate with an image set of 100 images is 68%

License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images

In this thesis, we propose a license plate recognition system and study the feasibility of using synthetic training samples to train convolutional neural networks for a practical application. First we develop a modular framework for synthetic license plate generation; to generate different license plate types (or other objects) only the first module needs to be adapted. The other modules apply variations to the training samples such as background, occlusions, camera perspective projection, object noise and camera acquisition noise, with the aim to achieve enough variation of the object that the trained networks will also recognize real objects of the same class. Then we design two convolutional neural networks of low-complexity for license plate detection and character recognition. Both are designed for simultaneous classification and localization by branching the networks into a classification and a regression branch and are trained end-to-end simultaneously over both branches, on only our synthetic training samples. To recognize real license plates, we design a pipeline for scale invariant license plate detection with a scale pyramid and a fully convolutional application of the license plate detection network in order to detect any number of license plates and of any scale in an image. Before character classification is applied, potential plate regions are un-skewed based on the detected plate location in order to achieve an as optimal representation of the characters as possible. The character classification is also performed with a fully convolutional sweep to simultaneously find all characters at once. Both the plate and the character stages apply a refinement classification where initial classifications are first centered and rescaled. We show that this simple, yet effective trick greatly improves the accuracy of our classifications, and at a small increase of complexity. To our knowledge, this trick has not been exploited before. To show the effectiveness of our system we first apply it on a dataset of photos of Italian license plates to evaluate the different stages of our system and which effect the classification thresholds have on the accuracy. We also find robust training parameters and thresholds that are reliable for classification without any need for calibration on a validation set of real annotated samples (which may not always be available) and achieve a balanced precision and recall on the set of Italian license plates, both in excess of 98%. Finally, to show that our system generalizes to new plate types, we compare our system to two reference system on a dataset of Taiwanese license plates. For this, we only modify the first module of the synthetic plate generation algorithm to produce Taiwanese license plates and adjust parameters regarding plate dimensions, then we train our networks and apply the classification pipeline, using the robust parameters, on the Taiwanese reference dataset. We achieve state-of-the-art performance on plate detection (99.86% precision and 99.1% recall), single character detection (99.6%) and full license reading (98.7%)

Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios

Benefiting from the rapid development of convolutional neural networks, the performance of car license plate detection and recognition has been largely improved. Nonetheless, challenges still exist especially for real-world applications. In this paper, we present an efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously. It is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time. Specifically, for unconstrained scenarios, an anchor-free method is adopted to efficiently detect the bounding box and four corners of a license plate, which are used to extract and rectify the target region features. Then, a novel convolutional neural network branch is designed to further extract features of characters without segmentation. Finally, recognition task is treated as sequence labelling problems, which are solved by Connectionist Temporal Classification (CTC) directly. Several public datasets including images collected from different scenarios under various conditions are chosen for evaluation. A large number of experiments indicate that the proposed method significantly outperforms the previous state-of-the-art methods in both speed and precision

BOUNDING BOX METHOD BASED ACCURATE VEHICLE NUMBER DETECTION AND RECOGNITION FOR HIGH SPEED APPLICATIONS

License plate detection and recognition is the one of the major aspects of applying the image processing techniques towards intelligent transport systems. Detecting the exact location of the license plate from the vehicle image at very high speed is the one of the most crucial step for vehicle plate detection systems. This paper proposes an algorithm to detect license plate region and edge processing both vertically and horizontally to improve the performance of the systems for high speed applications. Throughout the detection and recognition the original images are detected, filtered both vertically and horizontally, and threshold based on bounding box method. The whole system was tested on more than twenty five cars with various license plates in Indian style at different weather conditions. The overall accuracy rate of success recognition is 93% at sunlight conditions, 72% at cloudy, 71% at shaded weather conditions

Character recognition based on region pixel concentration for license plate identification

Budući da je postalo moguće izvoditi digitalnu obradu slike u kratkom vremenu, njezina uporaba u tehničkim sustavima postaje sve češća. Automatsko prepoznavanje registarskih oznaka jedan je takav primjer. Koristeći digitalnu obradu slike moguće je automatski detektirati i prepoznati znakove s registarske oznake vozila. Koraci u ovom procesu su predprocesiranje slike, detekcija pločice, segmentacija i prepoznavanje znakova. Ovaj proces obavlja algoritam koji uzima digitalne slike kao ulaz i daje tekstualni oblik znakova s registarskih oznaka kao izlaz. Postoji nekoliko metoda koje se koriste za izvođenje ovog procesa. Ove metode su objašnjene u ovom radu i jedna od njih je implementirana u C# programskom jeziku. Rezultati pokazuju da algoritam radi dobro u slučajevima bez puno deformacija ulazne slike. Međutim, još uvijek postoje slučajevi u kojima nepredvidiva priroda ulazne slike može uzrokovati neuspješnu detekciju ili prepoznavanje.Since it has become possible to perform digital image processing in a short period of time, its usage in technical systems is getting more common. Automatic license plate recognition is one such example. By using digital image processing it is possible to automatically detect and recognize characters on vehicle license plates. The steps taken in this process are image preprocessing, plate detection, character segmentation and recognition. This process is performed by the algorithm which takes the digital image as an input and gives textual form of license plate characters as an output. There are several methods used to perform this process. These methods are explained in this paper and one of them is implemented in C# programming language. The results show that the algorithm works fine in cases without much deformation of an input image. However, there are still cases where unpredictable nature of an input image can cause unsuccessful detection or recognition