Shading Removal of Illustrated Documents

Oliveira, Daniel M.; Lins, Rafael Dueire; Silva, Gabriel de França Pereira e

doi:10.1007/978-3-642-39094-4_35

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…Direct Interpolation In direct interpolation based approaches, image local regions belonging to the background are detected for illumination correction. Oliveira et al [19,20,18] assumed that a local region belonging to a digitized document background has a narrow Gaussian shape in its color histogram. With that assumption, initially, the local blocks belonging to the background were identified.…”

Section: Mask and Interpolationmentioning

confidence: 99%

“…Various studies in the literature show the importance of rectifying illumination distortion for a significantly better OCR performance [28,2,33,35]. The traditional methods for correcting the illumination distortions of the digitized documents start with estimating the background shades followed by removing the shades using a document surface reconstruction model [34,36,13,10,19,20,18,4,30]. These strategies can be divided into two prominent categories: mask-and-interpolation approaches [34,36,13,10], and without-mask-interpolation approaches [19,20,18,4,30].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional methods for correcting the illumination distortions of the digitized documents start with estimating the background shades followed by removing the shades using a document surface reconstruction model [34,36,13,10,19,20,18,4,30]. These strategies can be divided into two prominent categories: mask-and-interpolation approaches [34,36,13,10], and without-mask-interpolation approaches [19,20,18,4,30]. The former approaches figure out the location of text, and photo regions on the documents using either edge detection or binarization methods, followed by applying a mask that covers the text regions along with its adjacent area on the surface of the documents.…”

Section: Introductionmentioning

confidence: 99%

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Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

Jung

Hasan

Kim

2019

Lecture Notes in Computer Science

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0000−0001−7150−3746] , Muhammad Abul Hasan 2[0000−0002−1297−5054] , and Changick Kim 1[0000−0001−9323−8488]Abstract. In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts. Firstly, a topographic surface of an input digitized document is created using luminance value of each pixel. Then the shading artifact on the document is estimated by simulating an immersion process. The simulation of the immersion process is modeled using a novel diffusion equation with an iterative update rule. After estimating the shading artifacts, the digitized document is reconstructed using the Lambertian surface model. In order to evaluate the performance of the proposed algorithm, we conduct rigorous experiments on a set of digitized documents which is generated using smartphones under challenging lighting conditions. According to the experimental results, it is found that the proposed method produces promising illumination correction results and outperforms the results of the state-of-the-art methods. [Codes available at : https://github.com/seungjun45/Water-Filling]

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Section: Mask and Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

Jung

Hasan

Kim

2019

Lecture Notes in Computer Science

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“…Figure 7 demonstrates the capability of our shadow removal algorithm in removing shadows from document images. Oliveira et al [36] and Bako et al [37] developed shadow removal methods explicitly for document images, and Gong and Cosker [18] for general images. The results of the methods mentioned in the figure were obtained from Ref.…”

Section: Applicationsmentioning

confidence: 99%

Single image shadow removal by optimization using non-shadow anchor values

2019

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Shadow removal has evolved as a preprocessing step for various computer vision tasks. Several studies have been carried out over the past two decades to eliminate shadows from videos and images. Accurate shadow detection is an open problem because it is often considered difficult to interpret whether the darkness of a surface is contributed by a shadow incident on it or not. This paper introduces a color-model based technique to remove shadows from images. We formulate shadow removal as an optimization problem that minimizes the dissimilarities between a shadow area and its non-shadow counterpart. To achieve this, we map each shadow region to a set of non-shadow pixels, and compute an anchor value from the non-shadow pixels. The shadow region is then modified using a factor computed from the anchor value using particle swarm optimization. We demonstrate the efficiency of our technique on indoor shadows, outdoor shadows, soft shadows, and document shadows, both qualitatively and quantitatively.

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“…These methods are expected to provide potential means to correct the non-uniform illumination and color artifacts. In addition, there are other methods proposed to detect shadows [29][30][31][32], and remove shadows from document images or natural images [33][34][35][36][37][38], which is expected to benefit many text detection and recognition approaches reviewed in [39].…”

Section: Introductionmentioning

confidence: 99%

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Wang

Chen

2020

Sensors

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Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

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Shading Removal of Illustrated Documents

Cited by 19 publications

References 14 publications

Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

Single image shadow removal by optimization using non-shadow anchor values

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

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