Cross multi-scale locally encoded gradient patterns for off-line text-independent writer identification

Chahi, Abderrazak; Merabet, Youssef El; Ruichek, Yassine; Touahni, Raja

doi:10.1016/j.engappai.2019.103459

Cited by 20 publications

(9 citation statements)

References 47 publications

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“…Furthermore, when a paired T Test was performed between SRLDHD and SRLDSHD resulted in a P value of 0.0001, meaning that an extremely significant statistical difference exists. Finally, a paired T Test was conducted between SHD and 3QSHD and resulted in a P value of 0.0001, meaning that an extremely significant statistical difference exists here as well, but with the mean of [4] 63 Codebook of Graphemes combined with Edge-Hinge [16] 97 Edge-Hinge combinations [9] 81 Codebook of Graphemes combined with Edge-Hinge Combinations [9] 97 Contour-Hinge combined with Writer-Specific Grapheme Emission PDF [22] 83 SDS + SOH [24] 92.4 Quill-Hinge [11] 86 Junclets [8] 80.6 Junclets + Hinge [8] 89.8 BW-LBC [32] 94.4 CLGP [33] 97.6 *Dissimilarity GMM (DGMM) [37] 97.98 *GR-RNN [40] 98.8 6, an overview of the maximum accuracy achieved for all four techniques and results reported in the ICDAR 2017 writer identification competition for the MAP and Top-1 metrics. It is noticeable that there is a lot of room for improvement for both metrics.…”

Section: Icdar 2017 Writer Identification Competition Data Set Resultsmentioning

confidence: 99%

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Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

2021

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Section: Icdar 2017 Writer Identification Competition Data Set Resultsmentioning

confidence: 99%

“…Chahi et al [33] proposed Cross multi-scale Locally encoded Gradient Patterns (CLGP). This new feature extraction technique that represents better salient local writing structure operates at connected component sub-images of the writing sample.…”

Section: Other Statistical and Model-based Approachesmentioning

confidence: 99%

Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

2021

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“…Chahi et al in [30,31] proposed classic approaches emphasizing on extracting the desirable features. Authors in [30] proposed a Block Wise Local Binary Count (BW-LBC) operator stimulated by traditional LBP that characterizes multiple histograms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They utilized the nearest-neighbor classification using the Hamming distance and presented that their approach is better than the modern approaches. For writer identification, the authors in [31], proposed another classical feature extraction method. Their descriptor illustrates a salient feature for local writing structure and is applied to small connected regions of the sample.…”

Section: Literature Reviewmentioning

confidence: 99%

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

et al. 2021

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Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

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“…The features are broken down according to a set of criteria. The extraction features commonly used in devices for the identification of unique features are defined as follows [9]. Low level: image segmentation, tracking corner, detection of blob, feature extraction, scale-invariant transformation feature; curvature: active contours, parameterized shapes; image motion: image text extraction programs and tools, such as MathWorks, MATLAB, Scilab, and NumPy [10].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Saleem¹,

Abdulazeez²

2021

Computers, Materials &Amp; Continua

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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/styles. Previous studies have indicated that a word's attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The 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 texts. The fusion of these features with the features of convolutional neural networks (CNNs) results in a good vector of powerful features. Then, we reduce the features by selecting the best ones using a genetic algorithm. The normalization method is used to normalize the features and feed them to an artificial neural network classifier. Experimental 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 increased. Such a large data volume helps the system to retrieve extensive information about the nature of writing patterns. The 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 ResNet50. The whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different words. The HOG and CNN features achieve 94.2% accuracy for whole paragraphs with augmentation, 83.2% of accuracy for lines, and 78% accuracy for sub words. Thus, this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help identify writers on the basis of their sentences, words, and sub words.

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Cross multi-scale locally encoded gradient patterns for off-line text-independent writer identification

Cited by 20 publications

References 47 publications

Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Hybrid Trainable System for Writer Identification of Arabic Handwriting

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