Usage of Hough Transform for Expiry Date Extraction via Optical Character Recognition

Scazzoli, Davide; Bartezzaghi, Giulia; Uysal, Deniz; Magarini, Maurizio; Marcon, Marco

doi:10.1109/icaset.2019.8714306

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Numerous research papers, including [11,12,13,14], have previously proposed and discussed the method. However, this widely accepted and commonly practised approach has a significant limitation.…”

Section: Related Workmentioning

confidence: 99%

Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

shafi¹,

Atif²,

Shafi³

2023

Preprint

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Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

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Section: Related Workmentioning

confidence: 99%

Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

shafi¹,

Atif²,

Shafi³

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Automated Document Orientation Correction Using Skew and Inversion Rectification with Hough Line Transformation and Machine Learning

Shafi

Atif

shafi³

2023

Preprint

View full text Add to dashboard Cite

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 1000 images and achieved good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

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“…There is noise in the images of ancient Chinese books, and there is a tilt when scanning the images of ancient Chinese books. Remove the image noise of ancient Chinese books use a denoising method, and correct skew of the image employ the Hough transformation method which could find straight lines in an image [19,20]. The rough divided block is obtained by column projection followed by row projection of the layout of ancient Chinese books, and searching the connected component in the block [21,22].…”

Section: Rough Division Of Layout Images In Ancient Chinese Booksmentioning

confidence: 99%

Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

Tian

Sun

2020

IEEE Access

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To address the low segmentation accuracy caused by the rich glyph styles of ancient Chinese characters and the complex layout of ancient Chinese books, which affects the retrieval and recognition results, an algorithm for the layout image analysis of ancient Chinese books and Chinese character image segmentation is proposed. The initial segmentation results were obtained through the projection method of the layout of ancient Chinese books, and the connected component analysis of the above results was carried out to determine the rough divided blocks of under-segmentation and oversegmentation. Considering under-segmentation of adhesive Chinese characters, the improved K-means clustering method was used to segment adhesive blocks to obtain single-character images. To address the over-segmentation of character components separation, a method based on interval-valued hesitant fuzzy set is proposed. This method analyzed the features of the connected component in the block, characterized the over-segmentation connected component. The hesitant fuzzy distances between other connected components and the standard merge evaluation interval number were calculated in sequence. The connected component with the smallest distance was preferentially merged with the over-segmentation connected component until no over-segmentation connected component remained in the block. The experimental segmentation accuracy was 89.94%.

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Usage of Hough Transform for Expiry Date Extraction via Optical Character Recognition

Cited by 6 publications

References 5 publications

Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

Automated Document Orientation Correction Using Skew and Inversion Rectification with Hough Line Transformation and Machine Learning

Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

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