Weakly supervised precise segmentation for historical document images

Xie, Zhong; Huang, Yao‐Xiong; Jin, Lianwen; Liu, Yuliang; Zhu, Youwen; Gao, Liangcai; Zhang, Xiaode

doi:10.1016/j.neucom.2019.04.001

Cited by 33 publications

(19 citation statements)

References 22 publications

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“…Repeat the operations shown in steps III to VI until the evolution process reaches the state of convergence ALGORITHM 1: Shape prior embedded LSM. (IoU) value [26] between the ground truth of the target to be segmented is the shape prior required for the segmentation process. anks to the reconstruction capacity of KDE thought and the support capacity of the multiple shape prior database (as shown in Figure 4), the proposed method outputs correct segmentation results (as shown in the seventh row of Figure 3).…”

Section: Application On Real Infrared Images With Similar Targetmentioning

confidence: 99%

Shape Prior Embedded Level Set Model for Image Segmentation

Liu

Wang

Shi

2019

Journal of Electrical and Computer Engineering

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This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.

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Section: Application On Real Infrared Images With Similar Targetmentioning

confidence: 99%

Shape Prior Embedded Level Set Model for Image Segmentation

Liu

Wang

Shi

2019

Journal of Electrical and Computer Engineering

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“…Finally, character features were used to adjust the exact segmentation points in the fraction graph to achieve character segmentation. Xie et al [37] proposed a weakly supervised character segmentation method with recognition and guidance information in the attention region to solve the problem of touching and segmentation of historical Chinese documents and realized high-precision segmentation under strict cross and union ratios.…”

Section: Introductionmentioning

confidence: 99%

Character Detection and Segmentation of Historical Uchen Tibetan Documents in Complex Situations

et al. 2022

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Tibetan is a low-resource language, and Tibetan culture carried by historical Tibetan documents is an important part of Chinese civilization. The study of historical Tibetan documents is of great significance to the protection of Tibetan culture and the promotion of Chinese culture. Character segmentation is an important step in image analysis and recognition of historical Tibetan documents. However, the following three challenges prevent solving problems of character segmentation in historical Tibetan documents: 1) the text lines have different degrees of tilt and twist; 2) there are many complex situations such as overlapping, crossing, touching and breaking character strokes; and 3) these documents are written by different people with different stroke styles. To resolve these problems, we propose a character segmentation method based on key feature information for historical Tibetan documents. The proposed method consists of three parts: 1) projection and syllable point location information are used to shorten the text lines of historical Tibetan documents and establish a character block database; 2) the local baseline of the character block is detected by using the location information of syllable points or combined with horizontal projection and straight line detection, and the character block is divided into two areas above and below the baseline, and different segmentation methods are adopted; and 3) in view of the large difference in stroke styles, three stroke attribution distances are used to complete the attribution. The experimental results show that the method proposed in this paper can effectively solve the problem of character segmentation of historical Tibetan documents and achieve a better character segmentation effect, which also provides a reference for the relevant document character segmentation.

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“…Offline handwritten Chinese text recognition (OHCTR) is a challenging issue and has received significant attention from researchers [1][2][3]. The reason can be generally attributed to two important factors.…”

Section: Introductionmentioning

confidence: 99%

A Residual-Attention Offline Handwritten Chinese Text Recognition Based on Fully Convolutional Neural Networks

Wang¹,

Yang

Ding

et al. 2021

IEEE Access

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Offline handwritten Chinese text recognition is one of the most challenging tasks in that it involves various writing styles, complex character-touching, and large number of character categories. In this paper, we propose a residual-attention offline handwritten Chinese text recognition based on fully convolutional neural networks, which is segmentation-free handwritten recognition that avoids the impact of incorrect character segmentation. By designing a smart residual attention gate block, our model can help to extract important features, and effectively implement the training of deep convolutional neural networks. Furthermore, we deploy an expansion factor to indicate the trade-off between computing resources for model training and the ability of a gradient to propagate across multiple layers, and make our model training adapt to different computing platforms. Experiments on the CASIA-HWDB and ICDAR-2013 competition dataset show that our method achieves a competitive performance on offline handwritten Chinese text recognition. On the CASIA-HWDB test set, the character-level accurate rate and correct rate achieve 97.32% and 97.90% respectively. INDEX TERMSOffline handwritten recognition, Convolutional neural networks, Connectionist temporal classification, Residual attention.

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Weakly supervised precise segmentation for historical document images

Cited by 33 publications

References 22 publications

Shape Prior Embedded Level Set Model for Image Segmentation

Shape Prior Embedded Level Set Model for Image Segmentation

Character Detection and Segmentation of Historical Uchen Tibetan Documents in Complex Situations

A Residual-Attention Offline Handwritten Chinese Text Recognition Based on Fully Convolutional Neural Networks

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