Alphanumeric Characters Research Articles

Early perceptual locus of suppression during the attentional blink.

The attentional blink (AB) demonstrates that recognizing the second of two targets (T1 and T2) is difficult when they appear in close succession in a rapid serial visual presentation (RSVP) stream. The AB has been widely accepted as a suppression of T2 processing at the postperceptual stage. The current event-related potential study updates this view by demonstrating the existence of an early perceptual locus of suppression during the AB. Using line drawings of real-life objects as RSVP items, we required participants in Experiment 1 to either discriminate the exact identities or simply classify the object categories of T1 and T2, and in Experiment 2, we instructed participants to discriminate either T1 and T2 identities (dual-target task) or only T2 identity (single-target task) to invalidate the temporal expectation as an alternative account. The results of Experiments 1 and 2 showed that the T2-elicited first positive peak (P1) component was consistently decreased at Lag 3 whenever a dual-target, but not single-target, task was required, and the magnitude of this P1 suppression was significantly predictive of the behavioral AB magnitude in each dual-target task. When the RSVP items were substituted by classic but size-matched alphanumeric characters in Experiment 3, no P1 suppression was evident as expected, ruling out the large stimulus size as an alternative interpretation. These findings provide the strongest evidence to date that the AB can begin to suppress T2 processing at a very early perceptual stage, at least when observers encounter RSVP items of real-life objects, which calls for more flexible cognitive models for the AB. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

XAI-SALPAD: Explainable deep learning techniques for Saudi Arabia license plate automatic detection

In recent decades, automatic license plate recognition (ALPR) has emerged as a critical application of artificial intelligence in intelligent transportation systems (ITS), addressing a multitude of existing issues. Several algorithms have been developed to improve ALPR accuracy under a variety of conditions, each with its own set of strengths and limitations. The KSA intends to digitize traditional services as part of its Vision 2030 initiative. In this methodology, the ALPR system for accurately recognizing KSA's license plates (LP) leverages deep neural networks (DNNs) as a powerful technique, capable of performing effectively in unconstrained environments with images taken under various conditions, even when the training data is limited. The approach employs YOLOv8 to detect LPs and alphanumeric characters in real-time, followed by using a convolutional neural network (CNN) for character recognition. According to our finding, the YOLOv8 model outperforms other models in the identification of LPs, achieving remarkable accuracy and F1 scores (mAP@0.5 = 0.96 and mAP@0.95 = 0.97). As a result, YOLOv8 was chosen for SLP character recognition. This strategy is appropriate for ITS since it tackles a number of issues, like decreasing car theft and improving public safety. Furthermore, a locally interpretable agnostic model (LIME) offers justifications that help users better understand the model. The system's provision of clear explanations for its decisions not only satisfies performance standards but also corresponds with the increasing need for explainable AI, guaranteeing an efficient and responsible implementation.

Design and Evaluation of Controller-Based Raycasting Methods for Efficient Alphanumeric and Special Character Entry in Virtual Reality.

Alphanumeric and special characters are essential during text entry. Text entry in virtual reality (VR) is usually performed on a virtual Qwerty keyboard to minimize the need to learn new layouts. As such, entering capitals, symbols, and numbers in VR is often a direct migration from a physical/touchscreen Qwerty keyboard-that is, using the mode-switching keys to switch between different types of characters and symbols. However, there are inherent differences between a keyboard in VR and a physical/touchscreen keyboard, and as such, a direct adaptation of mode-switching via switch keys may not be suitable for VR. The high flexibility afforded by VR opens up more possibilities for entering alphanumeric and special characters using the Qwerty layout. In this work, we designed two controller-based raycasting text entry methods for alphanumeric and special characters input (Layer-ButtonSwitch and Key-ButtonSwitch) and compared them with two other methods (Standard Qwerty Keyboard and Layer-PointSwitch) that were derived from physical and soft Qwerty keyboards. We explored the performance and user preference of these four methods via two user studies (one short-term and one prolonged use), where participants were instructed to input text containing alphanumeric and special characters. Our results show that Layer-ButtonSwitch led to the highest statistically significant performance, followed by Key-ButtonSwitch and Standard Qwerty Keyboard, while Layer-PointSwitch had the slowest speed. With continuous practice, participants' performance using Key-ButtonSwitch reached that of Layer-ButtonSwitch. Further, the results show that the key-level layout used in Key-ButtonSwitch led users to parallel mode switching and character input operations because this layout showed all characters on one layer. We distill three recommendations from the results that can help guide the design of text entry techniques for alphanumeric and special characters in VR.

Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.

Non Alphanumeric Characters in the Title of Research Papers in Two Indian LIS journals

Non Alphanumeric Characters in the Title of Research Papers in Two Indian LIS journals

Vehicle Licence Number Plate Recognition Using Convolution Neural Network for Traffic Violators in Indonesia

In the context of rising traffic violations and the need for efficient traffic management, this study explores the application of CNN in the recognition of licence plates to identify traffic violators in Indonesia. Traditional traffic enforcement methods are labour-intensive and prone to human error, necessitating a more automated and reliable approach. This research aims to enhance the accuracy and efficiency of license plate recognition (LPR) systems. The proposed system involves capturing vehicle images from the Roboflow Universe collected in the Malang area for use. We also use a CNN model to recognize and extract the alphanumeric characters from the plates. The CNN architecture is designed and trained on a comprehensive dataset of Indonesian licence plates, taking into account the unique characteristics and variations in plate designs specific to the region. The research we are doing is detecting number plates to reduce traffic violations. The method used for detection is the CNN method. The datasets used are primary and secondary. The precision, recall, and F1 score metrics further validate the system's reliability and robustness in real-world traffic scenarios. The implementation of this CNN-based LPR system promises a substantial improvement in monitoring and penalizing traffic violators, contributing to better traffic law enforcement and road safety in Indonesia. The accuracy for CRR is 82, and the accuracy for LPR is 85,33. The accuracy for CCR is 76.55 for precisions, 78.51 for recall, and 81.72 for F1 score. The accuracy for LPR is 81.20 for precision, 87.37 for recall, and 83.56 for F1 score.

Vehicle License Plate Detection and Recognition using OpenCV and Tesseract OCR

License plate recognition (LPR) is essential as the number of vehicles increases and the human ability to accomplish this task is limited. If human labor is used to manage these, it will take a lot of time and energy and cause a discrepancy. License Plate Recognition (LPR) is an advanced technology that leverages optical character recognition (OCR) and various image processing methods to read vehicle license plates automatically. Typically, an LPR system comprises two primary components: detecting vehicles and their license plates and recognizing the alphanumeric characters displayed on those plates. This study explores the use of OpenCV for license plate detection and Tesseract OCR for character recognition. In this research, the dataset for training and testing the system included 100 license plates evenly split between plates featuring English and Lao characters. The Lao license plates presented unique complexities due to their specific characteristics. The experimental setup involved processing images of license plates taken from multiple angles. The system's performance was evaluated based on the speed and accuracy of line and character recognition. For English character plates, the recognition process took 0.12 seconds with an accuracy of 98.8%. In contrast, the Lao character plates required 0.24 seconds, achieving an accuracy rate of 89.42%.

The reliability of character recognition: An Australian & New Zealand expert-novice comparison study in the interpretation of chemically recovered serial numbers

The chemical recovery of a defaced serial number is a common forensic science practice, however it is not understood how proficient experts perform in correctly identifying recovered serial numbers. Understanding the accuracy of experts and how they compare to novices in character recognition can help to establish a baseline for this expertise. In this study an expert-novice comparison assessment was completed involving 118 test plates, each stamped with six randomised alphanumeric characters. The plates were defaced and chemically recovered before being viewed by multiple participants over six time intervals. A total of 3169 character inspections were completed. An assessment of confidence and error rates were calculated for both expert (trained) and novice (untrained) participants. Errors were counted when a participant interpreted a different character to that of the ground truth and believed the result was accurate for reporting. The results showed a similar (2.3 % and 2.4 %) error rate for the cohorts, however a statistical difference in confidence levels was recorded, demonstrating the more conservative nature of experts. This study aims to assist in validating practitioner interpretations, through addressing some forensic science criticisms, such as establishing error rates to routine scientific practices.

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations.

Cancer is a lethal disease that affects numerous people worldwide. Chemotherapy stands as one of the most effective treatment regimens to combat cancer. Nevertheless, anticancer drugs face a high failure rate due to safety and efficacy issues. Drug failure could be subdued by instigating drug leads with reduced toxicity and enhanced efficacy. Computer-aided drug discovery endorses drug leads in manoeuvring protein and ligand structures or representations. Simplified molecular input line entry system (SMILES) is a linear notation representing the three-dimensional structure of a molecule using symbols and alphanumeric characters. SMILES representation hoards rings and scaffold structures in its depiction. Mining ring and scaffold patterns from molecular SMILES would assist in ascertaining biological properties based on molecular patterns. Moreover, the emergence of artificial intelligence (AI) technologies would accelerate identification of efficient anticancer drug leads. AI algorithms proclaimed for their pattern recognition ability could be employed for identifying molecular patterns from SMILES representation, thereby enabling property prediction. Consequently, we developed a multilayer perceptron (MLP) model for the prediction of anticancer activity using SMILES of NCI-60 cancer growth inhibition data. Furthermore, the top 8 frequent scaffolds were identified on preliminary analysis of cancer growth inhibition data and ChEMBL drugs. The developed MLP model classified anticancer and nonanticancer compounds with a classification accuracy of 0.92. Also, benchmarking of the developed model with machine learning algorithms exhibited better performance of the MLP model.

Linguistic response surface methodology approach to measure the quality of nonlinear frame‐pixel and bit place‐based video steganography

AbstractSteganography refers to the practice of hiding sensitive information inside seemingly unrelated data sets. Steganography in the video is one of the best methods available for hiding data without compromising the film's appearance. For improved security and compatibility, the traditional system uses different video steganography techniques with linear or precise positions. Traditional linear video steganography practices face vulnerability, a lack of security, limited embedding options, and inadequate compatibility. Here nonlinear frame(s) and pixel positions based information hiding techniques have been developed to overwhelm the following. Both the nonlinear frame positions and nonlinear pixel positions are selected for the video‐based steganography. In the beginning, the nonlinear frame positions are selected through the key and the key may be with any prescribed range and alphanumeric characters. A single or more frames may be selected through the key and that entirely depends upon the corresponding run‐through. Then the nonlinear pixel and bit positions are also selected through a similar key. The proposed method is also compared with some former techniques and gives a magnificent result. Furthermore, a security analysis of the suggested algorithm has also been conducted using the differential attack method. To validate the suggested method and ensure that it is accurate, the author of this article made use of a very specific and innovative methodology known as the linguistic response surface methodology (LRSM). This model is framed based on achieving a few steganography assessment measures like PSNR, SSIM, and MSE metric values after incorporating hidden text in various nonlinear frames' nonlinear pixel locations of the video. The analysis of the variance using LRSM for PSNR, SSIM, and MSE response reveals very substantial results with confirmation.

A Recognition-Based Graphical Password System for Enhanced User Authentication

Abstract: Password-based authentication is a popular way for ensuring computer application security and privacy. Nonetheless, the "weakest link" in the authentication process is believed to be user-chosen weak passwords and hazardous input techniques. People generally utilize mnemonic or brief passphrases instead of random alphanumeric characters. Because internet and mobile applications are widely available, users can access them from any location, at any time, using any device. While this simplicity is generally appreciated, it also increases the likelihood that credentials will be compromised through Visual Hacking. The assault tactics can include employing external recording equipment or watching the victim personally in order to obtain their login information. In response to this issue, our team created a graphical password-based authentication mechanism to combat the threat of Visual Hacking. The system's security mechanism is three-tiered, featuring password verification as well as colour and pattern matching features. This means that even with numerous camera-based hacking attempts, potential attackers would have a tough time determining or limiting the password. In addition, we created and tested a system prototype to determine its usability. According to our trial results, our system can survive Visual Hacking better than previous authentication systems while maintaining a high level of security and usability.

BNVGLENET: Hypercomplex Bangla handwriting character recognition with hierarchical class expansion using Convolutional Neural Networks

Object recognition technology has made significant strides where recognizing handwritten Bangla characters including symbols, compounds form, etc. remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script, and individual’s unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. In comparison to other major languages, there are limited options for recognizing handwritten Bangla characters. This study has the potential to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language, which is spoken by over 200 million people worldwide. This paper aims to investigate the application of Convolutional Neural Networks (CNNs) for recognizing Bangla handwritten characters, with a particular focus on enlarging the recognized character classes. To achieve this, a novel challenging dataset for handwriting recognition is introduced, which is collected from numerous students’ handwriting from two institutions. A novel convolutional neural network-based approach called BNVGLENET is proposed in this paper to recognize Bangla handwritten characters by modifying the LeNet-5 and combining it with the VGG architecture, which has the advantage of significantly identifying the characters from Bengali handwriting. This study systematically evaluated the performance of models not only on custom novel dataset but also on the publicly available Bangla handwritten character dataset called the Grapheme dataset. This research achieved a state-of-the-art recognition accuracy of 98.2% on our custom testing vowel-consonant class and 97.5% on the custom individual class. The improvements achieved in this study bridge a notable disparity between the practical needs and the actual performance of Bangla handwritten character recognition systems.

Study On Advanced Vehicle Number Plate Detection Systems-A Review

In response to the rising volume of vehicles on roadways, there's an urgent demand for advanced technologies to bolster security and streamline traffic-related operations. This project introduces an innovative solution: an Advanced Automatic Number Plate Detection System. Leveraging Python, Easy OCR, and Open CV, this system aims to develop a sophisticated Automatic Number Plate Recognition (ANPR) mechanism capable of accurately detecting license plates, extracting alphanumeric characters, and providing actionable insights for various applications. Python is chosen for its versatility and ease of use, serving as the primary programming language. OpenCV is employed for its robust image processing tools and license plate detection capabilities, while Easy OCR simplifies character extraction from license plates. Key features include license plate detection through OpenCV algorithms, character recognition via Easy OCR, and real-time processing suitable for tasks like traffic monitoring and surveillance. The project follows a structured approach encompassing data collection, image pre-processing, machine learning model training, integration, and thorough testing. Anticipated outcomes include a fully operational ANPR system with high accuracy in license plate detection and character recognition, surpassing conventional methods. Its potential integration into real-world applications such as traffic management underscores its significance in addressing challenges posed by increasing vehicle numbers, with YOLO v8 utilized for precise outcomes. Keywords- Python, Open CV, YOLO, tensorflow, Machine learning, ANN, CNN, and Easy OCR

Design and Implementation of An Automated Car Plate Number Recognition System (ACPNRS)

The escalating global volume of vehicles on roadways necessitates innovative solutions for efficient traffic management, heightened security, and improved law enforcement. This research project centers on developing an Automated Car Plate Recognition System (ACPRS) to address these challenges. The ACPRS utilizes cutting-edge technologies in image processing, machine learning, and database integration for accurate and real-time license plate recognition. Beginning with an exploration of existing methodologies and technologies in the field, the research highlights their strengths and limitations. The conceptualization phase involves meticulous design, incorporating image preprocessing, license plate detection, character segmentation, optical character recognition (OCR), and database interaction. The research emphasizes the utilization of advanced image processing techniques, including deep learning algorithms, for robust license plate detection. Spectral analysis and character segmentation ensure reliable recognition in challenging conditions. OCR techniques enhance alphanumeric character interpretation, contributing to overall efficacy. The implementation phase involves software development, database setup, and integration with mobile applications for enhanced accessibility. Real-world applicability and scalability are pivotal considerations. The ACPRS is deployable in various contexts, such as parking management, security systems, and access control. Compatibility with mobile applications ensures widespread accessibility, broadening potential applications in diverse settings. Security and privacy measures include user authentication and data encryption to safeguard sensitive information. To validate the ACPRS’s effectiveness, comprehensive testing and evaluation use a diverse dataset. Real-world images, synthetic data, and publicly available datasets assess performance under different conditions. Evaluation metrics such as accuracy, speed, and robustness quantify capabilities and identify areas for improvement. In conclusion, this research project represents a significant contribution to automated vehicle identification. The proposed ACPRS, focusing on accuracy, real-time processing, and mobile accessibility, addresses critical challenges in traffic management, security, and law enforcement. Thorough exploration, design, implementation, and evaluation phases ensure understanding of the system’s capabilities and potential real-world applications. This work will be very significant to the developing country in managing their traffic and other related issues.

An Efficient and Robust Multi Directional Deep Learning Based Licence Plate Recognition

Smart cities must have all the important characteristics to achieve their intended goals. Proper traffic management and controlling, increased surveillance and safety, and enhanced management and avoidance of incidents must be the priorities of smart cities. Meanwhile, license plate recognition (LPR) has become the most debatable topic in the research community due to various real-time applications, such as “law enforcement, toll-free processing, access control, and traffic surveillance.” Automated LPR is a technique based on computer vision to recognize vehicles with their number plates. This study discusses various “Deep Learning based LPR” techniques to detect and identify “alphanumeric characters” in number plate. The projected model works on “license based detection” and “character recognition.” This technique uses “Optical Character Recognition (OCR)” technology to detect and extract the alphanumerical numbers from the license plate. This study is based on secondary data collected from various studies conducted on Licence Plate Recognition using various Deep Learning models published in databases like Google Scholar, Science Direct, NCBI, etc. Deep learning has been used widely in applications related to computer vision in recent years with great perfection. It is a great solution for modern and traditional image processing, object detection, and feature extraction issues. It has been widely used in different stages of LPR like character segmentation, license plate recognition, and OCR.

Glove-Based Classification of Hand Gestures for Arabic Sign Language Using Faster-CNN

Recently, American Sign Language has been widely researched to help disabled people to communicate with others. However; the Arabic Sign Language “ASL” has received much less attention. This paper has proposed a smart glove which has been designed using flex sensors to collect a dataset about hand gestures applying ASL. The dataset is composed of resistance and voltage measurements for the bending of the fingers to represent alpha-numeric characters. The measurements are manipulated using normalization and zero referencing methods to create the dataset. A Convolutional Neural Network ‘CNN’ composed of twenty-one layers is proposed. The dataset is used to train the CNN, and the Accuracy and Loss parameters are used to characterize its success. The dataset is classified with an average success rate of 95% based on the classification accuracy. Loss has decreased from 3 to less than 0.5. The proposed CNN layers have classified ASL characters with a reasonable degree of accuracy.

Development of etching paste for serial number restoration on aluminum engine block.

Engine numbers, which involve information regarding the engine type, production number, and year and place of manufacture, are used for identification purposes. Comprising of unique alphanumeric characters, the engine numbers are fully or partially obliterated especially in auto theft and smuggling cases to conceal the origin, identity, and owner of vehicles. The limitations of the current restoration techniques such as the difficulty of using chemical liquid etching in vertical sites, the restrictions of magnetic and optical methods, and the applicability of several techniques like electron backscatter diffraction only in the laboratory environment prompt the development of new techniques. In view of these limitations and the importance of restoring engine numbers in criminal investigations, this unique study aimed to develop an etching paste that would restore the effaced characters on a real aluminum alloy engine block. The characters which were cold-stamped on the engine block were milled at varying depths and restoration attempts were conducted using etching pastes formed with different chemicals and materials. The analyses indicate that the etching paste formed with 200 mg of perlite, 400 mg of iron powder, and 450 μL of 20 M NaOH provided restoration to a good extent. The prevention of over-etching through the controlling of the chemical reaction and the cost-effectiveness appears to be the advantages of this technique. The success of recovery on the real engine block, the facilitation of restoration on curved surfaces, and the chance of on-site usage will likely make the etching paste a widely used tool in serial number restoration.

Graphical Password Authentication

Abstract: A graphical password authentication is a form of authentication where users select a picture, image, or sequence as their password. Graphical passwords are an alternative to alphanumeric passwords in which users click on graphical images rather than typing alphanumeric characters to authenticate their identity. This method has gained popularity due to its ease of use and ability to provide better security than traditional alphanumeric passwords. This research paper provides a comprehensive study of graphical password authentication, including its history, types, advantages and disadvantages, security concerns, and future developments. The paper also includes a review of related research on graphical password authentication and its effectiveness in different settings.

Assistive Data Glove for Isolated Static Postures Recognition in American Sign Language Using Neural Network

Sign language recognition is one of the most challenging tasks of today’s era. Most of the researchers working in this domain have focused on different types of implementations for sign recognition. These implementations require the development of smart prototypes for capturing and classifying sign gestures. Keeping in mind the aspects of prototype design, sensor-based, vision-based, and hybrid approach-based prototypes have been designed. The authors in this paper have designed sensor-based assistive gloves to capture signs for the alphabet and digits. These signs are a small but important fraction of the ASL dictionary since they play an essential role in fingerspelling, which is a universal signed linguistic strategy for expressing personal names, technical terms, gaps in the lexicon, and emphasis. A scaled conjugate gradient-based back propagation algorithm is used to train a fully-connected neural network on a self-collected dataset of isolated static postures of digits, alphabetic, and alphanumeric characters. The authors also analyzed the impact of activation functions on the performance of neural networks. Successful implementation of the recognition network produced promising results for this small dataset of static gestures of digits, alphabetic, and alphanumeric characters.

Comparison of outline-based shape descriptors for alphanumeric characters by using piecewise linear functions: the case of vehicle license plates typeface

Comparison of outline-based shape descriptors for alphanumeric characters by using piecewise linear functions: the case of vehicle license plates typeface