Deep neural network for online writer identification using Beta-elliptic model

Dhieb, Thameur; Ouarda, Wael; Boubaker, Houcine; Alimi, Adel M.

doi:10.1109/ijcnn.2016.7727426

Cited by 17 publications

(4 citation statements)

References 39 publications

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“…Handwriting analysis has been an active area of research, such as handwriting recognition [11,13], writer identification [8], and signature verification [7,14]. When handwriting is captured using different acquisition techniques, it gives * rise to two handwriting categories: online and offline.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence-to-Sequence with Attention Mode

Rabhi¹,

Elbaati²,

Boubaker³

et al. 2021

Preprint

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Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

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Section: Introductionmentioning

confidence: 99%

“…The remaining layers use as an input their previous hidden state. For each time step, the output of the encoder is h t ϵ H calculated by equation(8).…”

mentioning

confidence: 99%

Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence-to-Sequence with Attention Mode

Rabhi¹,

Elbaati²,

Boubaker³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Handwriting analysis has been an active area of research such as handwriting recognition [11,13], writer identification [8], and signature verification [7,14]. When the handwriting is captured using different acquisition techniques, it makes * rise of two handwriting categories: an online type and an offline type.…”

Section: Introductionmentioning

confidence: 99%

Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence to Sequence Gated Recurrent Unit Model

Rabhi¹,

Elbaati²,

Boubaker³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The online signal is rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down. Their offline counterpart consists of a set of pixels. Thus, the online handwriting recognition accuracy is generally better than the offline one. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline handwriting. Our framework is based on sequence to sequence Gated Recurrent Unit (Seq2Seq GRU) model. The proposed system consists in extracting a hidden representation from an image using Convolutional Neural Network (CNN) and Bidirectional GRU (BGRU), and decoding the encoded vectors to generate dynamic information using BGRU. We validate our framework by an online recognition system applied on Latin, Arabic and Indian On/Off dual handwriting character database. To prove the performance<br>of the proposed system, we achieve a low error rate of point coordinates and a high accuracy rate of the LSTM recognition<br>system.

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“…In work [9], the use of subtractive clustering is suggested to capture the various writing styles/ prototypes of handwriting. The authors in [10,11] employ the utility of Beta-elliptic model, to describe the spatial and velocity profiles of the segmented sub-stroke. In another work, the writing styles present in the pre-segmented graphemes are represented by using the multi-fractal concept [12].…”

Section: Introductionmentioning

confidence: 99%

Online writer identification system using adaptive sparse representation framework

Venugopal

Sundaram

2020

IET biom.

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This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding to the sub-strokes of a set of enrolled writers. The second component is a similarity score, computed for each dictionary atom with regards to a given writer, that is related to the reconstruction error of the sub-stroke based feature vectors. The proposed identification is accomplished with an ensemble of support vector machines (SVMs), wherein the input to the SVM trained for a writer is obtained by incorporating the adapted saliency values of that writer on the document descriptor obtained via average pooling of sparse codes. Experiments performed on the IAM and IBM-UB1 online handwriting databases demonstrate the efficacy of the proposed scheme.

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Deep neural network for online writer identification using Beta-elliptic model

Cited by 17 publications

References 39 publications

Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence-to-Sequence with Attention Mode

Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence-to-Sequence with Attention Mode

Temporal Order and Pen Velocity Recovery for Character Handwriting Based on Sequence to Sequence Gated Recurrent Unit Model

Online writer identification system using adaptive sparse representation framework

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