Extraction Of Characteristics Research Articles

Biomedical named entity recognition based on multi-cross attention feature fusion.

Currently, in the field of biomedical named entity recognition, CharCNN (Character-level Convolutional Neural Networks) or CharRNN (Character-level Recurrent Neural Network) is typically used independently to extract character features. However, this approach does not consider the complementary capabilities between them and only concatenates word features, ignoring the feature information during the process of word integration. Based on this, this paper proposes a method of multi-cross attention feature fusion. First, DistilBioBERT and CharCNN and CharLSTM are used to perform cross-attention word-char (word features and character features) fusion separately. Then, the two feature vectors obtained from cross-attention fusion are fused again through cross-attention to obtain the final feature vector. Subsequently, a BiLSTM is introduced with a multi-head attention mechanism to enhance the model's ability to focus on key information features and further improve model performance. Finally, the output layer is used to output the final result. Experimental results show that the proposed model achieves the best F1 values of 90.76%, 89.79%, 94.98%, 80.27% and 88.84% on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chem, JNLPBA and BC2GM biomedical datasets respectively. This indicates that our model can capture richer semantic features and improve the ability to recognize entities.

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

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. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. 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 system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Cartoon copyright recognition method based on character personality action

Aiming at the problem of cartoon piracy and plagiarism, this paper proposes a method of cartoon copyright recognition based on character personality actions. This method can be used to compare the original cartoon actions with the action characteristics of pirated or copied cartoons to identify whether there is piracy or plagiarism. Firstly, an image preprocessing scheme for character extraction is designed. GrabCut interactive image segmentation algorithm was used to obtain cartoon characters, and then binarization and morphological processing were performed on the results. Secondly, a feature extraction scheme based on character profile, moving character and character pose is designed. By extracting the perimeter and area of the character contour, the length-to-width ratio of the smallest rectangle and the inclination angle of the contour, the character contour features are obtained. The three-dimensional coordinates are established by the central point position of the cartoon character in the two-dimensional image and the change of the character's zoom in and out, and the character's motion angle characteristics are calculated. By skeletonizing a character to obtain the pose characteristics, and using deburring operation to remove redundant branches, then extract the skeleton joint angle information. Finally, feature fusion is performed on the extracted features. The experimental results show that the proposed method breaks the limitation of the conventional single feature based recognition, and can better extract the character features including contour feature, motion feature and pose feature through multi-feature based extraction, so as to protect the cartoon copyright.

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.

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

Knowledge-Enhanced Historical Document Segmentation and Recognition

Optical Character Recognition (OCR) of historical document images remains a challenging task because of the distorted input images, extensive number of uncommon characters, and the scarcity of labeled data, which impedes modern deep learning-based OCR techniques from achieving good recognition accuracy. Meanwhile, there exists a substantial amount of expert knowledge that can be utilized in this task. However, such knowledge is usually complicated and could only be accurately expressed with formal languages such as first-order logic (FOL), which is difficult to be directly integrated into deep learning models. This paper proposes KESAR, a novel Knowledge-Enhanced Document Segmentation And Recognition method for historical document images based on the Abductive Learning (ABL) framework. The segmentation and recognition models are enhanced by incorporating background knowledge for character extraction and prediction, followed by an efficient joint optimization of both models. We validate the effectiveness of KESAR on historical document datasets. The experimental results demonstrate that our method can simultaneously utilize knowledge-driven reasoning and data-driven learning, which outperforms the current state-of-the-art methods.

Pepper Target Recognition and Detection Based on Improved YOLO v4

In order to improve visual recognition accuracy of pepper and provide reliable technical support for agricultural production, an improved YOLOv4 algorithm for pepper target recognition and detection was proposed in this paper. By adding Mosaic data enhancement and CBAM (Conventional block attention module) attention mechanism to the primitive character extraction network, the method enhanced the learning ability of the target detection algorithm, made the network effectively suppress the interference features, and increased the attention to effective features. To improve the accuracy of identification. The improved network model was trained, verified and tested on the self-made data set. The results showed that the proposed algorithm could effectively improve the accuracy of pepper recognition under natural light, and finally improved the mean Average Precision (mAP) of the existing YOLOv4 algorithm from 88.95% to 98.36%.

Hidden-information extraction from layered structures through terahertz imaging down to ultralow SNR.

Noninvasive inspection of layered structures has remained a long-standing challenge for time-resolved imaging techniques, where both resolution and contrast are compromised by prominent signal attenuation, interlayer reflections, and dispersion. Our method based on terahertz (THz) time-domain spectroscopy overcomes these limitations by offering fine resolution and a broadband spectrum to efficiently extract hidden structural and content information from layered structures. We exploit local symmetrical characteristics of reflected THz pulses to determine the location of each layer, and apply a statistical process in the spatiotemporal domain to enhance the image contrast. Its superior performance is evidenced by the extraction of alphabetic characters in 26-layer subwavelength papers as well as layer reconstruction and debonding inspection in the conservation of Terra-Cotta Warriors. Our method enables accurate structure reconstruction and high-contrast imaging of layered structures at ultralow signal-to-noise ratio, which holds great potential for internal inspection of cultural artifacts, electronic components, coatings, and composites with dozens of submillimeter layers.

SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models

Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.

Handwritten Character Recognition Using Deep Learning

Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduction of Deep learning has changed Optical Character Recognition by using models like Recurrent Neural Networks,convolutional neural network .In this paper i am gonna compare the different models like CNN model and CRNN model with current State of art model Transformer based Optical Character Recognition KeyWords :Transformers ,Convolution Recurrent Neural Network, Handwritten ,Optical Character Recognition

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.

Lithium Battery SOC Estimation Based on Multi-Head Attention Mechanism and GRU Algorithm

Pure electric vehicles have been widely used due to their non-pollution, low noise, high energy conversion efficiency and other advantages. SOC (State of Charge) is a crucial indicator for lithium batteries and pure electric vehicles. SOC cannot be directly measured. This article designs a new network structure. It is the GRU-Attention network structure. The stacked GRU algorithm in GRU-Attention network extracts the temporal characteristics of lithium battery test data, and the stacked multi-head self-attention network extracts the global information. The GRU-Attention network can avoid long-term dependency and gradient disappearance problems. The proposed network utilizes Stacked FFN as the dense layer. This article will test the network designed in the public data set at the University of Maryland. Simultaneously, this article compares the effects of different BatchSize on the performance of the algorithm. The network training process converges more effectively with a smaller BatchSize. Both too large and too small BatchSize have a negative impact on the generalization performance of the network. The extraction of the time-order character, however, may be hampered if the timestamp is too small. At the same time, the paper also compares the GRU-Attention network horizontally with the GRU and Attention networks. Eventually, the GRU-Attention network proposed in this article could better meet the estimate of the lithium battery SOC.

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.].

A deep learning-based mathematical modeling strategy for classifying musical genres in musical industry

Abstract Since the beginning of the digital music era, the number of available digital music resources has skyrocketed. The genre of music is a significant classification to use when elaborating music; the role of music tags in locating and categorizing electronic music services is essential. To categorize such a large music archive manually would be prohibitively expensive and time-consuming, rendering it obsolete. This study’s main contributions to knowledge are the following: This article will break down the music into many MIDI (music played on a digital musical instrument) movements, playing way close by analysis movement, character extraction from passages, and character sequencing from movement so that you may get a clearer picture of what you are hearing. The procedure includes the following steps: extracting the note character matrix, extracting the subject and segmentation grouping based on the note character matrix, researching and extracting beneficial characteristics based on the theme of the segments, and composing the feature sequence. It is challenging for the sorter to acquire spatial and contextual knowledge about music using traditional classification techniques due to its shallow structure. This study uses the unique pattern of input MIDI segments, which are used to probe the relationship between recurrent neural networks and attention. The approach for music classification is verified when paired with the testing precision of the same-length segment categorization; thus, gathering MIDI tracks 1920 along with genre tags from the network to construct statistics sets and perform music classification analysis.

Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps.

Lightweight Spatial-Spectral Network Based on 3D-2D Multi-Group Feature Extraction Module for Hyperspectral Image Classification

ABSTRACT Each pixel in the hyperspectral image contains tens to hundreds of narrow bands of detailed spectral information captured by a hyperspectral sensor. Hyperspectral image classification (HSIC) is widely used in remote sensing image analysis. Convolutional neural network (CNN) is one of the most commonly used deep learning methods based on visual data processing. In recent years, hyperspectral classification method based on the convolutional neural network has shown excellent performance. However, single-scale feature extraction may lose some important detail information and cannot guarantee that the best spatial features are captured. Multi-scale character extraction and increasingly deep network structures can improve classification accuracy, but also bring a large number of computational parameters, resulting in higher computational costs. In response to this series of problems, this paper proposes a lightweight spatial-spectral network based on 3D − 2D multi-group feature extraction module (MGFM) for HSCI. In the 3D-MGFM, the input feature maps are grouped and processed in parallel, and then the information of each channel is integrated by point-wise convolution to achieve spatial-spectral feature fusion. We use dilated convolutions with different dilation rate on the convolution kernel to improve the problem of feature information loss, so that we can better learn deep-level spatial-spectral features. In the 2D-MGFM, we selectively emphasize spectral feature information through SENet and extract multi-scale features based on Depthwise Separable Convolution (DSC) grouping. Experimental results on four hyperspectral datasets demonstrate that the proposed method achieves better classification performance than other state-of-the-art methods.

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.