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Supervised Feature Learning for Offline writer identification Using VLAD and Double Power Normalization

Computers, Materials & Continua 2023年第7期76卷 279-293页

作者： Dawei Liang Meng Wu Yan Hu College of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China JinCheng College Nanjing University of Aeronautics and AstronauticsNanjing211156China Engineering Research Center of Electronic Data Forensics Analysis Nanjing210031Jiangsu ProvinceChina

As an indispensable part of identity authentication,offline writer identification plays a notable role in biology,forensics,and historical document ***,identifying handwriting efficiently,stably,and quickly is still challenging due to the method of extracting and processing handwriting *** this paper,we propose an efficient system to identify writers through handwritten images,which integrates local and global features from similar handwritten *** local features are modeled by effective aggregate processing,and global features are extracted through transfer ***,the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and specific handwritten images,while the vector of locally aggregated descriptors with double power normalization is employed in aggregating local and global ***,handwritten image segmentation,preprocessing,enhancement,optimization of neural network architecture,and normalization for local and global features are exploited,significantly improving system *** proposed system is evaluated on Computer Vision Lab(CVL)datasets and the International Conference on Document Analysis and Recognition(ICDAR)2013 *** results show that it represents good generalizability and achieves state-of-the-art ***,the system performs better when training complete handwriting patches with the normalization *** experimental result indicates that it’s significant to segment handwriting reasonably while dealing with handwriting overlap,which reduces visual burstiness.

关键词： writer identification power normalization vector of locally aggregated descriptors feature extraction

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Hybrid Trainable System for writer identification of Arabic Handwriting

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Computers, Materials & Continua 2021年第9期68卷 3353-3372页

作者： Saleem Ibraheem Saleem Adnan Mohsin Abdulazeez Department of Information Technology Technical Informatics College of AkreDuhok Polytechnic UniversityDuhok42004Iraq

writer identification(WI)based on handwritten text structures is typically focused on digital characteristics,with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/*** studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or *** a result of the complexity of Arabic handwriting,segmenting and separating letters and strokes from a script poses a challenge in addition to WI *** this work,we propose new texture features for WI based on *** histogram of oriented gradient(HOG)features are modified to extract good features on the basis of the histogram of the orientation for different angles of *** fusion of these features with the features of convolutional neural networks(CNNs)results in a good vector of powerful ***,we reduce the features by selecting the best ones using a genetic *** normalization method is used to normalize the features and feed them to an artificial neural network *** results show that the proposed augmenter enhances the results for HOG features and ResNet50,as well as the proposed model,because the amount of data is *** a large data volume helps the system to retrieve extensive information about the nature of writing *** affective result of the proposed model for whole paragraphs,lines,and sub words is obtained using different models and then compared with those of the CNN and *** whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different *** HOG and CNN features achieve 94.2%accuracy for whole paragraphs with augmentation,83.2%of accuracy for lines,and 78%accuracy for sub ***,this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help

关键词： writer identification handwritten Arabic HOG features artificial neural network segmentation CNN features

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An Unsupervised writer identification Based on Generating Clusterable Embeddings

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Computer Systems Science & Engineering 2023年第8期46卷 2059-2073页

作者： M.F.Mridha Zabir Mohammad Muhammad Mohsin Kabir Aklima Akter Lima Sujoy Chandra Das Md Rashedul Islam Yutaka Watanobe Department of Computer Science and Engineering American International University BangladeshDhaka1229Bangladesh Department of Computer Science&Engineering Bangladesh University of Business&TechnologyDhaka1216Bangladesh Department of Computer Science and Engineering University of Asia PacificDhaka1216Bangladesh Department of Computer Science and Engineering University of AizuAizu-Wakamatsu965-8580Japan

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification *** to its importance,numerous studies have been conducted in various *** have established several learning methods for writer identification including supervised and unsupervised ***,supervised methods require a large amount of annotation data,which is impossible in most *** the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be *** paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text ***,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual *** proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification *** addition,traditional evaluation metrics are used in the proposed ***,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.

关键词： writer identification pairwise architecture clusterable embeddings convolutional neural network

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A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

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Computers, Materials & Continua 2021年第8期68卷 2727-2754页

作者： Saleem Ibraheem Saleem Adnan Mohsin Abdulazeez Zeynep Orman Department of Information Technology Technical Informatics College of AkreDuhok Polytechnic UniversityDuhok42004Kurdistan RegionIraq Department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34320Turkey

The writer identification(WI)of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist *** is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies,including old national and religious *** this study,we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on *** modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary ***,propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its *** projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary *** training stage involves taking a number of images for model *** images are selected randomly with different angles to generate four classes(0–90,90–180,180–270,and 270–360).The proposed segmentation approach achieves a high accuracy of 98.18%.The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of *** proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.

关键词： writer identification handwritten Arabic biometric systems artificial neural network segmentation skew detection model

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