Voting-Based Document Image Skew Detection

Boiangiu, Costin-Anton; Dinu, Ovidiu-Alexandru; Popescu, Claudia Laurenta; Constantin, Nicolae; Petrescu, Cătălin

doi:10.3390/app10072236

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Many researchers focused on pre-processing stages, like image resizing [61], blurring [64], thresholding [34], morphological operations [117], using OpenCV in conventional text detection methods. Preprocessing algorithms applied on the scanned document image depend on the several aspects such as image quality [87], scanned image resolution [101], [7], image skewness [87], [45], different format, and multiple layouts of the images and text [49], [39], [110].…”

Section: ) Optical Character Recognition (Ocr)mentioning

confidence: 99%

“…For noise removal, the frequently used method is to process the image through a low-pass filter and use it for future processing [34]. OpenCV-Python can be used to get rid of such noise, such as salt & pepper noise [34] and Gaussian noise [45], [95]. • Skew angle detection and correction: When a document is scanned, either automatically or by a person scanning a document, a slight tilt (skew) to a document is obvious [112].…”

Section: ) Optical Character Recognition (Ocr)mentioning

confidence: 99%

“…Projection profile analysis [45] Hough transforms [30], [87] and morphological transforms [96] are few methods for skew angle detection and correction mentioned in the literature.…”

Section: ) Optical Character Recognition (Ocr)mentioning

confidence: 99%

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Section: ) Optical Character Recognition (Ocr)mentioning

confidence: 99%

Section: ) Optical Character Recognition (Ocr)mentioning

confidence: 99%

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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“…(3) Projection profile analysis-based methods [ 9 , 10 , 11 ]. (4) Fourier transform based methods [ 12 , 13 , 14 , 15 ]. (5) Axis-parallel bounding box-based methods [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Deskewing Algorithm for Document Images

Bao

Yang

et al. 2022

Sensors

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Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

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“…(v) Clustering techniques, where segmentation depends purely on the characteristics of the image and is performed through feature clustering. In summary, it must be noted that every segmentation method is adapted to specific requirements; therefore, it must be the user who decides the algorithm most suited to his own requirements [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

Ruiz-Lendínez

Ureña-Cámara

Mesa-Mingorance

et al. 2021

IJGI

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There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.

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Voting-Based Document Image Skew Detection

Cited by 9 publications

References 18 publications

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

A Novel Adaptive Deskewing Algorithm for Document Images

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

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