Extraction Of Characteristics Research Articles

YOLO V5 for Vehicle Plate Detection in DKI Jakarta

The odd-even rule on vehicle number plates in DKI Jakarta aims to reduce congestion that occurs in DKI Jakarta. The application of these regulations is constrained by the limitations of the manual supervision function by officers. This problem can be overcome by implementing intelligence in the form of detecting number plate objects with the YOLO v5 algorithm and the character extraction process with Optical Character Recognition technology using Tesseract OCR. Object detection technology will detect objects in the form of vehicle plates. The OCR method can extract the characters on the number plate, the extraction results can be processed into parameter categorization so that the program can distinguish between vehicles that violate the rules and do not violate the rules automatically and more effectively and minimize errors. Based on this research, the average percentage of objects detected in each video is 92.38%, and the average confidence value obtained in object detection is between 75.55%. The success rate of the character extraction process on number plates is 95.45%, and the average proportion according to the detected number plate category is 97.2%. The implementation of the YOLO Algorithm has succeeded in detecting license plates with odd and even categories on videos that can provide signs and save violations of vehicles that violate the odd and even rules.

Extraction of Character from Visuals and Images Using OpenCV

To develop a computer vision system that can accurately extract characters from document pages and image data using the OpenCV library. The system is designed to process a wide range of visual inputs and extract characters with high precision and efficiency. The techniques used to implement the character extraction system, including pre-processing, feature extraction, and classification. The performance of the system is evaluated using a dataset of visual image data from a different type of visual inputs and the output of the system can be accurately extracting characters. In Future, Automated conversion of an image input into a machine-readable file.

A License Plate Recognition System with Robustness against Adverse Environmental Conditions Using Hopfield’s Neural Network

License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks.

Vehicle Number Plate Detection

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The resulting data is then stored in a database along with the time-stamp. The system is implemented and simulated in python, and its performance is tested on real image.

USAGE OF MASK R-CNN FOR AUTOMATIC LICENSE PLATE RECOGNITION

The subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems. As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made. Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.

Spectral pathways for effective delineation of mafic-ultramafic rocks by using PRISMA hyperspectral data

The Spectral Pathway Analysis (SPA) by using hyperspectral remote sensing data has a significant role in the demarcation of litho boundaries. The SPA is a 5 step continuous process 1) The reflectance spectral characterization through a controlled environment to the direct field sample along with satellite data acquisition 2) The generation of ICA and MNF 3) The endmember spectral extraction through the sub-pixel-based classification 4) The extraction of mineral characters from spectral absorption behavior 5) Mapping of litho units based on the integrative approaches by the data from the field, geology, and geochemical characters. In the present study, mafic and ultramafic complexes of the Bhavani shear zone which have a greater significance in the economically viable mineral aspects are taken into the consideration of lithological delineation by using PRISMA hyperspectral data. As part of SPA, ICA and MNF were generated using the PRISMA data and validated with geology, which shows a tremendous result in demarcating the lithological boundaries. Subpixel classification (PPI, SAM, LSU, MF, MTMF, TCIMF, and MITTCIMF) was performed and evaluated using ancillary data from field observation, XRF, and laboratory-based ASD spectra.

Named Entity Recognition Model Based on Feature Fusion

Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstly, Word2vec is used to extract word vectors, HMM is used to extract boundary vectors, ALBERT is used to extract character vectors, the Feedforward-attention mechanism is used to fuse the three vectors, and then the fused vectors representation is used to remove features by BiLSTM. Then multi-head attention is used to mine the potential word information in the text features. Finally, the text label classification results are output after the conditional random field screening. Through the verification of WeiboNER, MSRA, and CLUENER2020 datasets, the results show that the proposed algorithm can effectively improve the performance of named entity recognition.

Consumer Document Analytical Accelerator Hardware

Document scanning devices are used for visual character recognition, followed by text analytics in the software. Often such character extraction is insecure, and any third party can manipulate the information. On the other hand, near-edge processing devices are restrained by limited resources and connectivity issues. The primary factors that lead to exploring independent hardware devices with natural language processing (NLP) capabilities are latency during cloud processing and computing costs. This paper introduces a hardware accelerator for information retrieval using memristive TF-IDF implementation. In this system, each sentence is represented using a memristive crossbar layer, with each column containing a single word. The number of matching scores for the TF and IDF values was implemented using operational amplifier-based comparator accumulator circuits. The circuit is designed with a 180nm CMOS process, Knowm Multi-Stable Switch memristor model, and WOx device parameters. We compared its performance with that of a standard benchmark dataset. Variability and device-to-device related issues were also taken into consideration in the analysis. This paper concludes with implementing TF-IDF score calculation for applications such as information retrieval and text summarization.

Retracted: Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis.

[This retracts the article DOI: 10.1155/2021/2491116.].

Resource-Aware Scene Text Recognition Using Learned Features, Quantization, and Contour-Based Character Extraction

Resource-Aware Scene Text Recognition Using Learned Features, Quantization, and Contour-Based Character Extraction

Identifikasi Kualitas Kesegaran Ikan Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Ekstraksi Ciri Warna Hue, Saturation, dan Value (HSV)

Fish has a very high nutritional content and is needed by the human body, such as a protein. With the increasing production, and need for consumption of goodand fresh fish, irresponsible sellers take advantage of this situation by selling fish that are not fit for consumption, such as fish that are not fresh (rotten), fish that contain chlorine and formalin which can be detrimental to consumers. The purpose of this study was to determine how accurate the identification of fish freshness quality using the extraction of Hue, Saturation, and Value (HSV) color characteristics. The research method used is K-Nearest Neighbor (KNN) and is classified into several parts, namely, data collection techniques, needs analysis, design, training, and then testing. The image sample data used in this study amounted to 240 images consisting of fresh and non-fresh fish images, which will then be divided into training data and test data. The training data sample amounted to 220 images with a division of 110 fresh fish images and 110 non-fresh fish images, while the test data sample totaled 20 images with a division of 10 fresh fish images and 10 non-fresh fish images. Analysis of color features is carried out on the gills and head or the area around the eyes of the fish using Red, Green, and Blue (RGB) colors, which will be converted into Hue, Saturation, and Value (HSV) color spaces for the extraction and training processes to obtain results. The results showed that the use of HSV color character extraction was successfully applied with an accuracy value in the training of 94.09% and testing of 90%

Semisupervised Surveillance Video Character Extraction and Recognition With Attentional Learning Multiframe Fusion

Character extraction in the video is very helpful to the understanding of the video content, especially the artificially superimposed characters such as time and place in the surveillance video. However, the performance of the existing algorithms does not meet the needs of application. Therefore, the authors improve semisupervised surveillance video character extraction and recognition with attentional learning multiframe feature fusion. First, the multiframe fusion strategy based on an attention mechanism is adopted to solve the target missing problem, and the Dense ASPP network is introduced to solve the character multiscale problem. Second, a character image denoising algorithm based on semisupervised fuzzy C-means clustering is proposed to isolate and extract clean binary character images. Finally, for some video characters that may involve privacy, traditional and deep learning-based video restoration algorithms are used for characteristic elimination.

Class-Aware Sounding Objects Localization via Audiovisual Correspondence.

Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localization without category annotations, i.e., localizing the sounding object and recognizing its category. To address this problem, we propose a two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision. First, we propose to determine the sounding area via coarse-grained audiovisual correspondence in the single source cases. Then visual features in the sounding area are leveraged as candidate object representations to establish a category-representation object dictionary for expressive visual character extraction. We generate class-aware object localization maps in cocktail-party scenarios and use audiovisual correspondence to suppress silent areas by referring to this dictionary. Finally, we employ category-level audiovisual consistency as the supervision to achieve fine-grained audio and sounding object distribution alignment. Experiments on both realistic and synthesized videos show that our model is superior in localizing and recognizing objects as well as filtering out silent ones. We also transfer the learned audiovisual network into the unsupervised object detection task, obtaining reasonable performance.

Character Extraction and Character Type Identification from Summarised Story Plots

Identifying the characters from free-form text and understanding the roles and relationships between them is an evolving area of research. They have a wide range of applications, from summarising narrations to understanding the social network from social media tweets, which can help in automation and improve the experience of AI systems like chatbots and much more. The aim of this research is twofold. Firstly, we aim to develop an effective method of extracting characters from a story summary, to develop a set of relevant features, then, using supervised learning algorithms, to identify the character types. Secondly, we aim to examine the efficacy of unsupervised learning algorithms in type identification, as it is challenging to find a dataset with a predetermined list of characters, roles, and relationships that are essential for supervised learning. To do so, we used summary plots of fictional stories to experiment and evaluate our approach. Our character extraction approach successfully improved on the performance reported by existing work, with an average F1-score of 0.86. Supervised learning algorithms successfully identified the character types and achieved an overall average F1-score of 0.94. However, the clustering algorithms identified more than three clusters, indicating that more research is needed to improve their efficacy.

Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features

Javanese script (Hanacaraka) is one of the cultures owned by Indonesia. Javanese script is found in temples, inscriptions, cultural and prehistoric sites, ancient Javanese manuscripts, Gulden series banknotes, street signage, and palace documents. Javanese script has a form with an article, and the use of reading above the script is a factor that affects the character detection process. Punctuation marks, clothing, Swara script, vowels, and consonants are parts of the script that are often found in Javanetest scripts. Preserving Javanese script in the digital era, of course, must use technology that can support the digitization of Javanese script through the script detection process. The concept of script image is the image of Javanese script in ancient manuscripts. The process of character detection using certain techniques can be carried out to extract characters so that they can be read. Detection of Javanese characters can be found by finding a testing image. Here, we had been used 10 words images consisting of 3 to 5 syllables with the vowel aiu. Dataset process by Linear Binary Pattern (LBP) feature extraction, which is used to characterize images and describe image textures locally. LBP has been used in r=4 and preprocessing is also done by thresholding with d=0.3. This process can be done using the K-Nearest Neighbor algorithm. In 10 datasets of Javanese script words, an average accuracy value of 90.5% was obtained. The accuracy value of 100% is the highest and 50% is the lowest.

RDTS Noise Reduction Method Based on ICEEMDAN-FE-WSTD

Aiming at the problem of temperature measurement error caused by the noise signal on the Raman-based distributed optical fiber temperature sensor (RDTS), a noise reduction method based on the improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy entropy, and the wavelet soft threshold denoising (ICEEMDAN-FE-WSTD) is proposed. First, ICEEMDAN is used to decompose the input signal, and noise intrinsic mode functions (IMFs) are determined by calculating the FE value of each IMF. Then, noise IMFs are denoised by the WSTD method. Finally, the noise reduction results are obtained by combining the denoised IMFs with the remaining useful signal IMFs for reconstruction. The advantages of the proposed method in terms of denoising and character extraction are verified by the simulation signal experiment with different signal-to-noise ratios (SNRs). The temperature measurement experiments are carried out with heating areas set at 40 °C, 45 °C, 50 °C, 55 °C, and 60 °C. Taking the 40 °C temperature measurement experiment as an example, after the temperature measurement signal is denoised by the proposed method, the maximum temperature measurement error in the heating area is reduced by 1.07 °C, the SNR is increased by 4.83 dB, and the root-mean-square error (RMSE) is reduced by 0.77. The temperature drift of RDTS signals after noise reduction by the proposed method is also effectively suppressed, and the maximum temperature measurement error at 2.28 km is reduced by 1.43 °C. The denoising results of the proposed method are compared with the other six noise reduction methods in this article, and the proposed method has the best performance.

Implementation of OCR (Optical Character Recognition) Using Tesseract in Detecting Character in Quotes Text Images

The development of technology in Indonesia is currently increasingly advanced in people's lives and cannot be avoided. The use of Artificial Intelligence in helping humans in dealing with problems is growing. Humans can take advantage of computer/smartphone media in today's technological era. One of its uses is Optical Character Recognition. This research is motivated by the problem where the running system requires development in terms of technology to detect characters in the quote text image, because the previous system still performs manual input. Optical Character Recognition has been widely used to extract characters contained in digital image media. The ability of OCR methods and techniques is very dependent on the normalization process as an initial process before entering into the next stages such as segmentation and identification. The image normalization process aims to obtain a better input image so that the segmentation and identification process can produce optimal accuracy. To get maximum results, it takes several pre-processing stages on the image to be used. To achieve this, it is necessary to perform Optical Character Recognition which can be done using Tesseract-OCR. The OCR program that was created was successfully used to scan or scan a quote text image if the document was lost or damaged, and it could save time for creating, processing and typing documents.

Design of Piano Intelligent Teaching System Based on Neural Network Algorithm

In recent years, with the improvement of economic level, music art has become a major focus of extracurricular teaching. Students of all ages are included. However, the high cost of piano teaching and the unique one-to-one teaching method of teachers and students lead to the lack of piano education resources. Learning piano has become a luxury activity. Therefore, using computer multimedia software to teach piano has become a feasible way to alleviate the current contradiction. For piano teaching, the main difficulties are the differences between teachers and students (i.e., the data change at both ends due to the network), the instability of the network system, and the neural network algorithm can solve these difficulties. Based on this point, this work aims to introduce neural network algorithm into piano teaching intelligent system. This paper first introduces the theoretical basis of the neural network algorithm, then expounds the algorithm flow and general framework of the algorithm in speech recognition, and explains the split explanation in combination with five aspects: preprocessing, character extraction, acoustic model, linguistic model, and decoding. Then, it introduces the system design of intelligent piano teaching and describes the general system requirements and product architecture. Finally, the intelligent piano teaching system is tested and applied to prove the effectiveness of the system. I hope this intelligent piano teaching system can provide more convenience for piano teaching.

An effective detection approach for phishing websites using URL and HTML features

Today's growing phishing websites pose significant threats due to their extremely undetectable risk. They anticipate internet users to mistake them as genuine ones in order to reveal user information and privacy, such as login ids, pass-words, credit card numbers, etc. without notice. This paper proposes a new approach to solve the anti-phishing problem. The new features of this approach can be represented by URL character sequence without phishing prior knowledge, various hyperlink information, and textual content of the webpage, which are combined and fed to train the XGBoost classifier. One of the major contributions of this paper is the selection of different new features, which are capable enough to detect 0-h attacks, and these features do not depend on any third-party services. In particular, we extract character level Term Frequency-Inverse Document Frequency (TF-IDF) features from noisy parts of HTML and plaintext of the given webpage. Moreover, our proposed hyperlink features determine the relationship between the content and the URL of a webpage. Due to the absence of publicly available large phishing data sets, we needed to create our own data set with 60,252 webpages to validate the proposed solution. This data contains 32,972 benign webpages and 27,280 phishing webpages. For evaluations, the performance of each category of the proposed feature set is evaluated, and various classification algorithms are employed. From the empirical results, it was observed that the proposed individual features are valuable for phishing detection. However, the integration of all the features improves the detection of phishing sites with significant accuracy. The proposed approach achieved an accuracy of 96.76% with only 1.39% false-positive rate on our dataset, and an accuracy of 98.48% with 2.09% false-positive rate on benchmark dataset, which outperforms the existing baseline approaches.

Topological aspects of the critical three-state Potts model

We explore the topological defects of the critical three-state Potts spin system on the torus, Klein bottle and cylinder. A complete characterization is obtained by breaking down the Fuchs–Runkel–Schweigert construction of 2D rational CFT to the lattice setting. This is done by applying the strange correlator prescription to the recently obtained tensor network descriptions of string-net ground states in terms of bimodule categories (Lootens et al 2021 SciPost Phys. 10 053). The symmetries are represented by matrix product operators (MPO), as well as intertwiners between the diagonal tetracritical Ising model and the non-diagonal three-state Potts model. Our categorical construction lifts the global transfer matrix symmetries and intertwiners, previously obtained by solving Yang–Baxter equations, to MPO symmetries and intertwiners that can be locally deformed, fused and split. This enables the extraction of conformal characters from partition functions and yields a comprehensive picture of all boundary conditions.