Recovery of drawing order from multi-stroke English handwritten images based on graph models and ambiguous zone analysis

Dinh, Minh Le; Yang, Hyung-Jeong; Lee, Guee-Sang; Kim, Soo-Hyung; Do, Luu-Ngoc

doi:10.1016/j.eswa.2016.08.017

Cited by 17 publications

(20 citation statements)

References 17 publications

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“…Nevertheless, the effectiveness of the proposed investigation on loops was not clear enough as they did not show any practical results of handwriting recognition and since the valuation time was high. On the other hand, the use of the skeleton approach has given good results and a more rapid response compared to the contour method [9,12,21,28,32]. Based on the edge continuity relation [28], the authors suggested three main steps.…”

Section: Related Workmentioning

confidence: 99%

“…Their work was based on limited assumptions about start / end points, ambiguous zones and double traces segments, which gave rise to a difficult decision for obtaining the right trajectory. In [9], the authors considered that start point detection and skeleton separation represented a hard task. In addition, there was a higher complexity of searching the smoothest path at the junction zone.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the effectiveness of the proposed investigation on loops is not clear enough as they do not show any practical results of handwriting recognition and the valuation time is high. On the other hand, the use of skeleton approach gives good results and a more rapid response compared to contour method [9,12,21,28,32]. Based on Edge Continuity Relation (ECR) [28], the authors propose 3 main steps.…”

Section: Related Workmentioning

confidence: 99%

“…Their work is based on limited assumptions about start/end points, ambiguous zone and double trace segments, which give rise to a difficult decision for obtaining the right trajectory. In [9], the authors consider that start point detection and skeleton separation is a hard task. In addition, there is a higher complexity of searching the smoothest path at junction zone.…”

Section: Related Workmentioning

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

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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“…Drawing order recovery (DOR) is widely discussed in various fields, including off-line to on-line handwriting recognition [1][2][3][4], robot based in-home learning companion for writing [5], video-based storytelling animation [6], etc. In spite of various methods proposed before [1,3,7,8], etc., DOR from static images is still a great challenge.…”

Section: Introductionmentioning

confidence: 99%

Drawing Order Recovery from Trajectory Components

Yang

Zhou

Sun

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In spite of widely discussed, drawing order recovery (DOR) from static images is still a great challenge task. Based on the idea that drawing trajectories are able to be recovered by connecting their trajectory components in correct orders, this work proposes a novel DOR method from static images. The method contains two steps: firstly, we adopt a convolution neural network (CNN) to predict the next possible drawing components, which is able to covert the components in images to their reasonable sequences. We denote this architecture as Im2Seq-CNN; secondly, considering possible errors exist in the reasonable sequences generated by the first step, we construct a sequence to order structure (Seq2Order) to adjust the sequences to the correct orders. The main contributions include: (1) the Img2Seq-CNN step considers DOR from components instead of traditional pixels one by one along trajectories, which contributes to static images to component sequences; (2) the Seq2Order step adopts image position codes instead of traditional points' coordinates in its encoder-decoder gated recurrent neural network (GRU-RNN). The proposed method is experienced on two well-known open handwriting databases, and yields robust and competitive results on handwriting DOR tasks compared to the state-of-arts.

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Recovery of drawing order from multi-stroke English handwritten images based on graph models and ambiguous zone analysis

Cited by 17 publications

References 17 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

Drawing Order Recovery from Trajectory Components

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