Clutter noise removal in binary document images

Agrawal, Motilal; Doermann, David

doi:10.1007/s10032-012-0196-6

Cited by 11 publications

(3 citation statements)

References 25 publications

(13 reference statements)

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“…Noise reduction in ancient documents is a particularly challenging research subject [35]. Generally, it is not possible to re-do the binarization process of the documents and it is necessary to work with images that have a low information-to-noise ratio [32].…”

Section: Noise Correctionmentioning

confidence: 99%

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Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the "ima" kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification.Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the "ima" kanji with 3% False positives.

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Section: Noise Correctionmentioning

confidence: 99%

“…Noise removal [32]. This step is performed to eliminate small regions produced by dirt in the original documents or minor scanning problems.…”

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confidence: 99%

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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“…The literature, however, proposes only blind deconvolution procedures, as they are useful in the highly relevant fields of biomedical imaging [45], aerial photography [46], and optical astronomy [47]. Another important application of space-varying motion deblurring is the improvement of document scanning quality [48,49].…”

Section: The Case Of Variable Space Varying Motion Blurmentioning

confidence: 99%

A motion blur compensation algorithm for 2D DIC measurements of deformable bodies

Lavatelli

Turrisi

Zappa

2018

Meas. Sci. Technol.

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Digital image correlation (DIC) is a widely adopted optical technique to detect motions and deformations dealing with vision-based systems. Considering DIC dynamic applications, motion blur represents one of the most relevant sources of measurement uncertainty. This paper proposes an innovative approach to compensate the effect of motion blur on images coming from deformable bodies, where the amount of blur (w) varies within the single frame. A subset-based technique is developed to estimate and then remove motion blur for each image region. The algorithm is validated both on synthetic images and experimental data, demonstrating its effectiveness at improving the accuracy of the displacements measured by DIC.

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Document Image Analysis

Santosh

2018

Document Image Analysis

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Clutter noise removal in binary document images

Cited by 11 publications

References 25 publications

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

A motion blur compensation algorithm for 2D DIC measurements of deformable bodies

Document Image Analysis

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