A Fast Skew Correction Technique for Camera Captured Business Card Images

Mollah, Ayatullah Faruk; Basu, Subhadip; Das, Nibaran; Sarkar, Ram; Nasipuri, Mita; Kundu, Mahantapas

doi:10.1109/indcon.2009.5409427

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…Techniques involving classical image processing have been able to sufficiently dewarp linearly warped images of documents [9]- [11], but have been forced into a corner when non-linearly warped or folded-document images have been tested on them [12]. While some of them have passed with considerably good values in one evaluation metric, they have been severely penalized in another.…”

Section: Previous Workmentioning

confidence: 99%

A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

Bandyopadhyay

Dasgupta

Das

et al. 2020

Preprint

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Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUNet dataset after being trained on just 8 percent of the data used in previous methods.

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Section: Previous Workmentioning

confidence: 99%

A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

Bandyopadhyay

Dasgupta

Das

et al. 2020

Preprint

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“…A system developed for only English capital letters in [5].A first skew correction technique for Camera Captured Business Card Images for Mobile Devices is proposed in [6].Optical Character Recognition remains very difficult for many languages.…”

Section: Previous Workmentioning

confidence: 99%

Optical Character Recognition based on Template Matching

Hossain¹,

Afrin²

2019

GJCST

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This paper presents an innovative design for Optical Character Recognition (OCR) from text images by using the Template Matching method.OCR is an important research area and one of the most successful applications of technology in the field of pattern recognition and artificial intelligence.OCR provides full alphanumeric visualization of printed and handwritten characters by scanning text images and converts it into a corresponding editable text document. The main objective of this system prototype is to develop a prototype for the OCR system and to implement The Template Matching algorithm for provoking the system prototype. In this paper, we took alphabet (A-Z and a-z), and numbers (0-1), grayscale images, bitmap image format were used and recognized the alphabet and numbers by comparing between two images. Besides, we checked accuracy for different fonts of alphabet and numbers. Here we used Matlab R 2018 a software for the proper implementation of the system.

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“…Text/Graphics Separation for Business Card Images for Mobile Devices is proposed in [15]. A Fast Skew Correction Technique for Camera Captured Business Card Images is proposed in [16]. Segmentation of Camera Captured Business Card Images for Mobile Devices is proposed in [17].…”

Section: Previous Workmentioning

confidence: 99%

Optical Character Recognition from Text Image

Jana¹,

Chowdhury²,

Islam³

2014

IJCATR

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Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact character using feature matching between the extracted character and the template of all characters as a measure of similarity.

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A Fast Skew Correction Technique for Camera Captured Business Card Images

Cited by 16 publications

References 4 publications

A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

Optical Character Recognition based on Template Matching

Optical Character Recognition from Text Image

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