Recover Writing Trajectory from Multiple Stroked Image Using Bidirectional Dynamic Search

Qiao, Yu; Yasuhara, Makoto

doi:10.1109/icpr.2006.984

Cited by 11 publications

(14 citation statements)

References 14 publications

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“…A study based on matching cost function that is the summation of positional distortions cost and directional difference cost between the template stroke and its matching path. A bidirectional search algorithm based on dynamic programming has been used to find best path [22]. A three phase approach used to restore writing order where first phase include neural network to obtain possible edge continuity relations, second phase include double traced lines identification and last third phase find candidates of single stroked paths by depth first search [21].…”

Section: Introductionmentioning

confidence: 99%

A combined static and dynamic feature extraction technique to recognize handwritten digits

Sharma

2015

Vietnam J Comput Sci

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The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (<1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

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Section: Introductionmentioning

confidence: 99%

A combined static and dynamic feature extraction technique to recognize handwritten digits

Sharma

2015

Vietnam J Comput Sci

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“…6 (i). Note that this condition at = 1 is similar to the condition of the fitting-based recovery method like [5], because the method relies on a single model from the same class. Table I shows that the value of has a little effect for Method A.…”

Section: Quantitative Evaluationmentioning

confidence: 93%

“…Exceptionally, Qiao et al [5] proposed a method which can recover the stroke order of multi-stroke characters. Their method is a kind of model-fitting methods [6], [7], where a one-dimensional stroke model is fitted nonlinearly to a two-dimensional input image.…”

Section: Introductionmentioning

confidence: 99%

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On the possibility of instance-based stroke recovery

Iwakiri

Shiraishi

Feng

et al. 2012

2012 International Conference on Frontiers in Handwriting Recognition

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Abstract-This paper tackles the stroke recovery problem, which is a typical ill-posed reverse problem, by an instancebased method. The basic idea of the instance-based stroke recovery is to refer to the drawing order of a similar instance. The instance-based method has a strong merit that it can deal with multi-stroke characters and other complex characters without any special consideration. However, it requires a sufficient numbers of instances to cover those various characters. As an initial trial of the instance-based stroke recovery method, this paper describes the principle of the method and then provides several experimental results. The experimental results indicate the potential of the proposed method on recovering the drawing order of complex characters, as expected.

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“…The recovered writing trajectory is e y1 → e y2 , ..., → e yn . To speed up the process, we identify the candidate start and end points for each stroke at first and then do bidirectional search from these candidates [21]. The ECR (Edge Continuity Relation) analysis method proposed in [22] is used to simplify graph model G.…”

Section: Inference By Dynamic Programmingmentioning

confidence: 99%