Automatic writer identification from text line images

This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) Root Mean Square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.

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“…This technique has been successfully used in different applications (e.g. [64]). Linear Discriminant Analysis (LDA).…”

Section: Parametric Discriminant Analysismentioning

confidence: 98%

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Hamedi

Salleh

Ting

et al. 2018

IEEE Trans. Affective Comput.

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“…Handwriting, which is a type of personal biometric data, has distinctive features or habits that cannot be imitated by another person (Kırlı & Gülmezoğlu, 2012). This study aimed to make person recognition/verification and gender classification from handwritten images independently of the text content.…”

Section: Discussionmentioning

confidence: 99%

Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods

Agduk¹,

Aydemir²

2022

AIP

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The writing process, in which feelings and thoughts are expressed in writing, differs from person to person. Handwriting samples, which are very easy to obtain, are frequently used to identify individuals because they are biometric data. Today, with human-machine interaction increasing by the day, machine learning algorithms are frequently used in offline handwriting identification. Within the scope of this study, a dataset was created from 3250 handwritten images of 65 people. We tried to classify collected handwriting samples according to person and gender. In the classification made for person and gender recognition, feature extraction was done using 32 different transfer learning algorithms in the Python program. For person and gender estimation, the classification process was carried out using the random forest algorithm. 28 different classification algorithms were used, with DenseNet169 yielding the most successful results, and the data were classified in terms of person and gender. As a result, the highest success rates obtained in person and gender classification were 92.46% and 92.77%, respectively.

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“…Local features are calculated directly and provide adequate information about the basic composition and shape of a character. Extraction of local features require segmentation and this approach is applied in [6][7][8][9][10].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Writer recognition systems use global features such as texture, curvature and slant features [4], [5] and also local features such as graphemes, allographs and connected components to identify the writers. Extraction of local features require segmentation and this approach is applied in [6][7][8][9][10].…”

Section: Figure 1 Steps Of Writer Identification Processmentioning

confidence: 99%

Review of Offline Text Independent Writer Identification Techniques

2016

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This paper reviews various text independent writer identification techniques through offline documents. Different features extraction methods are discussed. Classification approaches that are mainly used for identification by the researchers and verification by different groups and individuals are presented. Identification rates achieved by the reviewed papers are tabulated and analyzed. A survey of different databases used in the reviewed papers is performed. Application of writer identification in different language domains is also discussed. Future directions for the automated writer identification are presented in the end.

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Automatic writer identification from text line images

Cited by 16 publications

References 31 publications

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods

Review of Offline Text Independent Writer Identification Techniques

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