A New Parallel Thinning Algorithm with Stroke Correction for Odia Characters

Pujari, Arun K.; Mitra, Chandana; Mishra, Sourav

doi:10.1007/978-3-319-07353-8_48

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…They had also shown interest in zoned based and make SVM as the classifier and reported 95% recognition rate. Some algorithm for classification was applied on thinned characters by Arun K. Pujari et al in [14]. All the calculation was done to skeletonize images of character in order perform stroke preservation.…”

Section: Related Work Donementioning

confidence: 99%

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

2019

IJITEE

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Handwritten Character Recognition is most challenging area of research, in which for various aspects a little enhancement can be always achieved. It is due to the irregularity of writing and shapes of different class user’s orientation affects the recognition rate. In this paper we have taken the complexity of Odia handwritten character recognition and successfully resolve with Principal Component Analysis (PCA). Here we had adopted a model in which the importance of symmetric axis chords in recognition of unconstrained handwritten characters is established. This symmetric axis chords are drawn along both row-wise and column-wise among the points one end to other. In addition to we have calculated the statistical feature as Euclidian distance, Hamilton distance which drawn from the midpoint of the symmetric chord to nearest pixel of the character. Apart from it we have also reported the angular values from the centroid of the image to the character pixel. This empirical model also harnessed the PCA over the feature set and perform the dimension reduction to the feature set which later termed as the key feature set. A certain series of experiment was carried on for the proper implementation of proposed technique, henceforth we have taken the standard Handwritten Database from various research institutes. Lastly on simulation analysis Radial Basis Function Neural Network (RBFNN) has been reported as to achieve high recognition rate through Gaussian kernel and a comparison among them has also reported here with.

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Section: Related Work Donementioning

confidence: 99%

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

2019

IJITEE

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“…Most existing methods in character skeleton extraction focus on either local visual rules (Zhang and Suen 1984;Pujari, Mitra, and Mishra 2014;Dong et al 2017) or distance measurements (Zou and Yan 2001). These methods focus on low-level features in local regions, but when reading, humans turn to concern the skeletons of characters subconsciously and ignore the colors or widths of strokes.…”

Section: Related Work Deep Side Outputsmentioning

confidence: 99%

“…The former methods execute iteratively to delete pixels on the boundary strokes until centered lines remain, and the deletion or retention of stroke pixels depends on the connectivity in the neighborhood, such as the ZhangSuen algorithm (Zhang and Suen 1984). An improved ZhangSuen algorithm was designed for Odia characters, combining with stroke correction (Pujari, Mitra, and Mishra 2014). In (Dong et al 2017), stroke continuity detection serves as a preprocessing step for thinning.…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters

Wang

Liu

2018

AAAI

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Structural analysis of handwritten characters relies heavily on robust skeletonization of strokes, which has not been solved well by previous thinning methods. This paper presents an effective fully convolutional network (FCN) to extract stroke skeletons for handwritten Chinese characters. We combine the holistically-nested architecture with regressive dense upsampling convolution (rDUC) and recently proposed hybrid dilated convolution (HDC) to generate pixel-level prediction for skeleton extraction. We evaluate our method on character images synthesized from the online handwritten dataset CASIA-OLHWDB and achieve higher accuracy of skeleton pixel detection than traditional thinning algorithms. We also conduct skeleton based character recognition experiments using convolutional neural network (CNN) classifiers on offline/online handwritten datasets, and obtained comparable accuracies with recognition on original character images. This implies the skeletonization loses little shape information.

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“…Segmentation techniques based on the tubular structure of blood vessels include topology refinement [1][2][3][4] and distance transformation [5][6][7][8]. To obtain an accurate blood vessel model, the tubular structure of the blood vessel is the basis of modeling, and the multi-scale segmentation method [9,10] is based on the eigenvalues of the Hessian matrix.…”

Section: Introductionmentioning

confidence: 99%

Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs

Yong-wei

Kwak

2022

Applied Sciences

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The segmentation algorithm of cerebrovascular magnetic resonance angiography (MRA) images based on deep learning plays an essential role in medical study. Traditional segmentation algorithms face poor segmentation results and poor connectivity when the cerebrovascular vessels are thinner. An improved segmentation algorithm based on deep convolutional networks is proposed in this research. The proposed segmentation network combines the original 3D U-Net with the maximum intensity projection (MIP), which was transformed from the corresponding patch of a 3D MRA image. The MRA dataset provided by Jeonbuk National University Hospital was used to evaluate the experimental results in comparison with traditional 3D cerebrovascular segmentation methods and other state–of–the–art deep learning methods. The experimental results showed that our method achieved the best test performance among the compared methods in terms of the Dice score when Inception blocks and attention modules were placed in the proposed dual-path networks.

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A New Parallel Thinning Algorithm with Stroke Correction for Odia Characters

Cited by 12 publications

References 7 publications

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters

Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs

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