Character Segmentation Research Articles

SentinelGuard Pro: Deploying Cutting‐Edge FusionNet for Unerring Detection and Enforcement of Wrong Parking Incidents

ABSTRACTWrong parking incidents pose a pervasive challenge in urban environments, disrupting the smooth flow of traffic, compromising safety and contributing to various logistical issues. Unauthorized parking occurs when vehicles are parked in locations not designated for such purposes, leading to a myriad of problems for both authorities and the general public. This research introduces a pioneering approach to confront the persistent challenge of unauthorized parking incidents in urban environments. The study focuses on harnessing the advanced capabilities of the FusionNet model to enhance the accuracy of license plate detection. This paper introduces the YOLO v8 Model, a deep learning architecture designed to enhance urban parking management by accurately detecting vehicles parked in unauthorized slots. The objective is to enhance parking management efficiency by accurately detecting vehicles and their occupancy status in designated parking areas. The methodology begins with data collection and preprocessing of images of parking spaces, followed by the training of YOLO v8 to identify vehicles and parking spaces in real time. Leveraging a diverse dataset encompassing various parking scenarios, including instances of unauthorized parking, the model achieves an accuracy of 98.50% in identifying vehicles outside designated areas. This model segments characters from detected license plates, enabling the accurate extraction of alphanumeric information associated with each vehicle. The integrated system provides timely identification of parking violations and facilitates effective enforcement actions through captured license plate data. Results demonstrate the model's effectiveness in real‐world scenarios, showcasing its potential for improving urban safety and efficiency. The implementation of FusionNet in the Python programming language, the proposed solution aims to streamline parking management, improve compliance with parking regulations and enhance overall urban mobility., with robust precision 96.17%, specificity 97.42% and sensitivity 96.19%, surpassing other MobileNet, CNN, ANN, DNN and EfficientNet models.

Double Hydrophilic Hyperbranched Copolymer-Based Lipomer Nanoparticles: Copolymer Synthesis and Co-Assembly Studies

Double hydrophilic, random, hyperbranched copolymers were synthesized via reversible addition–fragmentation chain transfer (RAFT) polymerization of oligo(ethylene glycol) methyl ether methacrylate (OEGMA) and 2-(dimethylamino)ethyl methacrylate (DMAEMA) utilizing ethylene glycol dimethacrylate (EGDMA) as the branching agent. The resulting copolymers were characterized in terms of their molecular weight and dispersity using size exclusion chromatography (SEC), and their chemical structure was confirmed using FT-IR and 1H-NMR spectroscopy techniques. The choice of the two hydrophilic blocks and the design of the macromolecular structure allowed the formation of self-assembled nanoparticles, partially due to the pH-responsive character of the DMAEMA segments and their interaction with -COOH end groups remaining from the chain transfer agent. The copolymers showed pH-responsive properties, mainly due to the protonation–deprotonation equilibria of the DMAEMA segments. Subsequently, a nanoscopic polymer–lipid (lipomer) mixed system was formulated by complexing the synthesized copolymers with cosmetic amphiphilic emulsifiers, specifically glyceryl stearate (GS) and glyceryl stearate citrate (GSC). This study aims to show that developing lipid–polymer hybrid nanoparticles can effectively address the limitations of both liposomes and polymeric nanoparticles. The effects of varying the ionic strength and pH on stimuli-sensitive polymeric and mixed polymer–lipid nanostructures were thoroughly investigated. To achieve this, the structural properties of the hybrid nanoparticles were comprehensively characterized using physicochemical techniques providing insights into their size distribution and stability.

Research on automatic segmentation and recognition of single characters in original rubbings based on intelligent recognition of oracle bone inscriptions

This paper proposes an effective segmentation and recognition method using YOLOv8 to solve the problem of segmentation and recognition of Oracle rubbings images. Draw sub-pixel edge contours through spot detection, corner detection, and edge detection (such as Canny and Sobel), and combine grayscale, opening operations, binarization, and Gaussian filtering to remove noise and repair contours, extract image features, and establish preprocessing Model. Use this model to preprocess the original oracle rubbing image and train it based on YOLOv8. The results show that this method can effectively segment and identify oracle bones characters.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

A deep learning approach for line-level Amharic Braille image recognition

Braille, the most popular tactile-based writing system, uses patterns of raised dots arranged in cells to inscribe characters for visually impaired persons. Amharic is Ethiopia’s official working language, spoken by more than 100 million people. To bridge the written communication gap between persons with and without eyesight, multiple Optical braille recognition systems for various language scripts have been developed utilizing both statistical and deep learning approaches. However, the need for half-character identification and character segmentation has complicated these systems, particularly in the Amharic script, where each character is represented by two braille cells. To address these challenges, this study proposed deep learning model that combines a CNN and a BiLSTM network with CTC. The model was trained with 1,800 line images with 32 × 256 and 48 × 256 dimensions, and validated with 200 line images and evaluated using Character Error Rate. The best-trained model had a CER of 7.81% on test data with a 48 × 256 image dimension. These findings demonstrate that the proposed sequence-to-sequence learning method is a viable Optical Braille Recognition (OBR) solution that does not necessitate extensive image pre and post processing. Inaddition, we have made the first Amharic braille line-image data set available for free to researchers via the link: https://github.com/Ne-UoG-git/Am-Br-line-image.github.io.

Stone inscription image segmentation based on Stacked-UNets and GANs

To overcome the challenges posed in effectively extracting stone inscriptions characterized by highly self-similarity between the foreground and background, a character image segmentation framework is proposed that integrates Stacked-UNets and Generative Adversarial Networks (GANs). Initially, a convolutional rule tailored for self-similar feature extraction is introduced to enhance the image detail segmentation. Subsequently, in improving the stacked units, multi-scale character masks with Spatial Transformer Network (STN) are added to guide the character segmentation. Finally, with two GANs and datasets, the character recognition and generation capabilities of the Stacked-UNets are trained, and further constructing its character segmentation and restoration abilities. Ultimately, the edge segmentation performance was enhanced, both in extracting characters from stone inscriptions with self-similar blocks and in effectively recovering partially incomplete characters. Compared to state-of-the-art methods, the Stacked-UNets demonstrates improvements of 6.41%, 7.39%, 8.43%, 8.11%, 4.13%, and 2.58% across six indicators, respectively.

An intelligent character segmentation system coupled with deep learning based recognition for the digitization of ancient Tamil palm leaf manuscripts

Palm-leaf manuscripts, rich with ancient knowledge in areas such as history, art, and medicine, are vital cultural treasures, making their digitization essential for preserving this heritage. Digitization of these organic and fragile manuscripts is required to safeguard the essential ancient data. This requires optimal character segmentation and recognition algorithms. A limited number of studies have been carried out in Tamil character recognition in literature. Handling row-overlapped characters, noise introduced due to lightning issues, and dirt, as well as the removal of punch holes, auto-cropping the content, filtering out noisy or improper segmentation, etc. are the essential concerns carried out in our proposed work. This work is executed as a four-step process (1) Palm Leaf Manuscript Acquisition (2) Pre-Processing (3) Segmentation of Tamil Characters and (4) Tamil Character Recognition. During acquisition, the scanners are used for recording palm leaf manuscripts from the Tamil Nadu-oriented manuscript library. In the Pre-processing step, the Fast Non-Local Means (Fast-NLM) method, paired with median filtering is used for Denoising the scanner output image. Later, the pixels that make the characters and borders (i.e., the foreground) are identified using Sauvola thresholding. The proposed methodology introduces efficient techniques to remove Punch hole impressions from the pre-processed image, and to crop the written content from the edges. After pre-processing, the Segmentation of Tamil Characters is performed as a three-step process (a) Manuscript (b) Line, and (c) character segmentation, which addresses conjoined lines, partially/completely empty segmentations that are not previously addressed by existing techniques. This work introduces an Augmented HPP line-splitting algorithm that accurately segments written lines, handling wrong segmentation cases that were previously not considered by existing techniques. The system achieves an average segmentation accuracy of 98.25%, which far outperforms existing techniques. It also proposes a novel Punch hole removal algorithm that can locate and remove the punch-hole impressions in the manuscript image. This algorithm, along with the automated content cropping technique, increases recognition accuracy and eliminates any manual labor needed. These features make the proposed methodology highly suitable for real-time archaeological and historical researches that include manuscripts. All 247 letters and 12 numeric digits are analyzed and separated into 125 distinct writable characters. In our work, characters are segmented and used for recognition of all 247 letters and 12 digits in Tamil using a multi-class CNN with 125 classes, which drastically reduces the complexity of the neural network compared to having 257 output nodes. It offered a notable performance of 96.04% accuracy. As compared with existing Tamil and other character recognitions, this work is effective in essence of considering real-time images and the increased number of characters used.

Hetero-associative Memory Based New Iraqi License Plate Recognition

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول على الصورة والشكل واللون، بالإضافة إلى الصعوبات مثل ضعف دقة الصورة ، الصورة الباهتة ، الإضاءة السيئة، التباين المنخفض، يجب التغلب عليها. اقترحت هذه الورقة نموذجًا باستخدام تعديل الذاكرة الترابطية ثنائية الاتجاه (MBAM)، وهي نوع واحد من الذاكرة الترابطية غير المتجانسة، وتعمل MBAM على مرحلتين)مرحلتي التعلم والتقارب) للتعرف على اللوحة، ويمكن لهذا النموذج المقترح التغلب على تلك الصعوبات بسبب قدرة الذاكرة الترابطية لـ MBAM على قبول الضوضاء وتمييز الصور المشوهة، وكذلك سرعة عملية الحساب نظرًا لصغر حجم الشبكة. نتيجة دقة تحديد منطقة اللوحة هي 99.6٪، ودقة تجزئة الأحرف 98٪، والدقة المحققة للتعرف على الأحرف هي100 ٪ في ظروف مختلفة.

The effect of silicon incorporation on hybrid polyurethane‐imide elastomers with graphite flakes: Unique microstructures, conducting properties, and mechanical properties

AbstractIn this paper, a series of silicon hybrid polyurethane‐imides (SPUI) with high stability were constructed by introducing diphenylsilanediol (DPSD) and imidization effect, in which the segment character and hydrogen bonding were adjusted. Various “sea‐island” microphase separations, similar evolution from blooming flower to bud like microdomains was originating from the hard segment clusters, where the segment distribution was labeled and visualized by the stacking of silicon heteroatom. Comparing with pure polyurethane (PU), tensile strength and elongation at break for SPUIs increased from 0.71 ± 0.04 to 17.67 ± 1.84 MPa and from 84.39% ± 6.79% to 202.38% ± 14.50%, respectively. Interestingly, the silicon‐induced aggregates of hard segments in SPUI/graphite flakes (SPUI/GFs), densely crowded around GFs except for some dispersions, revealing similar coupling agent characters of SPUI and self‐affinity with GFs. Thus, the high performance was achieved due to adjustable percolation and cross‐linking networks. Percolation thresholds were decreased from 2.04 to 0.61 wt% and the conductivity of 1.0 × 100 S/m at GFs contents of 5.0 wt% for SPUIz/GFs‐500 was achieved, 2–3 orders of magnitude higher than that of SPUIz/GFs‐2000 except for high stability in conductivity, far lower than those of reported composites. Finally, this study was of great significance for developing functional PU materials with high performance.

Segmentation Analysis of Staycationers in Jakarta

Staycation has been growing and continuously gaining popularity in Indonesia during and after the COVID-19 pandemic. This study aims to identify segments of staycation tourists/staycationers in Jakarta, one of Indonesia's most popular staycation destinations. Using quantitative methods, this study implements a proportional sampling design based on accommodation types with a total of 130 valid and reliable online questionnaire surveys. This study identifies factors and clusters using lifestyle variables from Activity, Interest, and Opinion (AIO) Model and examines their correlation with demographic, geographic, and behaviour segmentation. Data were analysed by descriptive statistics, including factor analysis, cluster analysis, discriminant analysis, and cross-tabulation analysis. The findings revealed four types of staycationers in Jakarta: (1) I-want-everything staycationers, (2) independent and attention seekers, (3) indulgent shopaholics, and (4) culture and local adventurers. Profiles and characters of each staycationer segment were analysed, giving significant knowledge about staycation market, particularly in Jakarta. Thus, this study offers valuable information for tourism and hospitality businesses to focus on the most potential market segment. Keywords: AIO model, lifestyle segmentation, market segmentation, marketing, staycation

Thermal stimuli response diphenylmethane diisocyanate‐based polyurethane elastomer via adjustable silicon‐induced distinctive microstructure

AbstractThe segment character of polyurethanes (PUs) determines the micromorphology and properties. The effective segment regulation can customize the favorable performance of PUs. Herein, silicon covalent‐modified polyurethanes (MPUs) were prepared by incorporating diphenylmethane diisocyanate (MDI) and diphenyl silanediol (DPSD) into PU molecules as hard segments via a two‐step method. Distinctive microphase separation structures from sheet morphology to radial sheet or withered flower‐like microdomain were formed by adjusting hydrogen bonding of hard segments and post‐treatment techniques. Thus, with the addition of DPSD in hard segments, the thermo‐optic responses for MPUs occurred due to the different microphase evolution under thermal effect. And with the further addition of DPSD, the effect of thermal stimuli on the transmittance gradually weakened. It was very clear that with increasing the temperature, the transmittance of MPUs showed a gradual increase owing to melting the microcrystals or destroying the ordered accumulation of hard segments. Meanwhile, the high mechanical properties were obtained, higher 15 times tensile strength and more than 100 times elastic modulus than those of pure PU as well as the excellent thermal stability. In sum, this study was of great significance for developing new functionalized PU materials.Highlights Polyurethanes were prepared by inducing modified silicon functionality. The distinctive microphase separation evolution of MPUs was found. Thermal stimuli responses showed a gradual decrease due to different structure. High mechanical properties and thermal stability were also obtained.

Keycap character detection model based on target classification

Abstract The production process of keycaps may result in surface character defects. The existing keycap character defect detection technology has low efficiency and accuracy, hindering the automation of keycap manufacturing. Addressing the challenge of extracting the entire keyboard from illuminated regions, this paper proposes a method using image feature points for template matching to extract the region of interest. Subsequently, leveraging prior knowledge, the paper addresses character segmentation and extraction on the keyboard. For character detection, the paper preprocesses images and classifies them by using an S-VGG network, yielding corresponding labels.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

Moving Vehicle Number Plate Detection

Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This preprocessing step may include operations such as resizing, grayscale conversion, noise reduction, and contrast enhancement. Next, object detection algorithms such as Haar cascades or deep learning-based techniques like YOLO (You Only Look Once) are utilized to detect candidate regions in the image/frame that potentially contain license plates. After candidate regions are identified, a series of image processing techniques such as morphological operations, contour detection, and character segmentation are employed to isolate and extract the license plate characters. Machine learning models like Support Vector Machines (SVM), Convolutional Neural Networks (CNNs) are then applied to recognize the characters on the license plate.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed Books

This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss function allows the segmentation network to accurately extract character strokes that can be attributed to the typeface of the movable metal type used for printing. Our method calculates deviation between the boundary of predicted character strokes and the counterpart of the ground-truth strokes. Diverging from traditional Euclidean distance metrics, our approach determines the deviation indirectly utilizing boundary pixel-value difference over a Gaussian-smoothed version of the stroke boundary. This approach helps extract characters with smooth boundaries efficiently. Through experiments, it is confirmed that the proposed method not only smoothens stroke boundaries in character extraction, but also effectively eliminates noise and outliers, significantly improving the clarity and accuracy of the segmentation process.

Handwritten Optical Character Recognition to Digital Text Conversion

Handwritten Optical Character Recognition (OCR) is a crucial technology for converting handwritten text into digital format. This process involves detecting and interpreting handwritten characters from images or scanned documents. In this project, we focus on OCR for three languages: Kannada, Telugu, and Hindi. The system utilizes machine learning algorithms, specifically trained neural networks, to recognize and transcribe handwritten characters accurately. The OCR system preprocesses the input images, applies character segmentation, and then classifies each segment into the corresponding character class. Post- processing techniques may be applied to improve accuracy and handle noise. The converted digital text can then be further processed, analyzed, or stored as needed. This technology has various applications in digitizing historical documents, automating data entry tasks, and enabling accessibility for visually impaired individuals

A top-down character segmentation approach for Assamese and Telugu handwritten documents

A top-down character segmentation approach for Assamese and Telugu handwritten documents

Automatic Number Plate Recognition

Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.