Robust skew estimation using straight lines in document images

Koo, Hyung Il; Cho, Nam Ik

doi:10.1117/1.jei.25.3.033014

Cited by 6 publications

(3 citation statements)

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“…We will continue to extend our investigation to develop more suitable CNN models for document classification tasks. This would involve exploring more CNN architectures such as inception 12 and DenseNet; 77 more document image preprocessing techniques such as Zemouri and Chibani's 78 binarization for degraded document images, Koo and Cho's skewness estimation, 79 and image augmentation 80 to increase the amount of "groundtruth" training data; and more document image databases such as a medieval document image collection. 81 Second, our investigations revealed impact trends of coupling preprocessing the CNN's performance and demonstrated that the impact of coupling preprocessing could stem from different factors.…”

Section: Discussionmentioning

confidence: 99%

Investigating coupling preprocessing with shallow and deep convolutional neural networks in document image classification

2021

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Convolutional neural networks (CNNs) are effective for image classification, and deeper CNNs are being used to improve classification performance. Indeed, as needs increase for searchability of vast printed document image collections, powerful CNNs have been used in place of conventional image processing. However, better performances of deep CNNs come at the expense of computational complexity. Are the additional training efforts required by deeper CNNs worth the improvement in performance? Or could a shallow CNN coupled with conventional image processing (e.g., binarization and consolidation) outperform deeper CNNbased solutions? We investigate performance gaps among shallow (LeNet-5, -7, and -9), deep (ResNet-18), and very deep (ResNet-152, MobileNetV2, and EfficientNet) CNNs for noisy printed document images, e.g., historical newspapers and document images in the RVL-CDIP repository. Our investigation considers two different classification tasks: (1) identifying poems in historical newspapers and (2) classifying 16 document types in document images. Empirical results show that a shallow CNN coupled with computationally inexpensive preprocessing can have a robust response with significantly reduced training samples; deep CNNs coupled with preprocessing can outperform very deep CNNs effectively and efficiently; and aggressive preprocessing is not helpful as it could remove potentially useful information in document images.

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Section: Discussionmentioning

confidence: 99%

Investigating coupling preprocessing with shallow and deep convolutional neural networks in document image classification

2021

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“…For realistic test images, we have scanned printed documents with flatbed-scanners, and applied the following procedures to the scanned images: skew correction [31], text line extraction [32], and word segmentation [30]. In building the testset, we have used multi-script words by concatenating English, Chinese, and Korean characters as shown in Figure 5.…”

Section: B Test Word Datasetmentioning

confidence: 99%

Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter

et al. 2020

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In this paper, we present a new multilingual Optical Character Recognition (OCR) system for scanned documents. In the case of Latin characters, current open source systems such as Tesseract provide very high accuracy. However, the accuracy of the multilingual documents, including Asian characters, is usually lower than that for Latin-only documents. For example, when the document is the mix of English, Chinese and/or Korean characters, the OCR accuracy is lowered than English-only because the character/text properties of Chinese and Korean are quite different from Latin-type characters. To tackle these problems, we propose a new framework using three neural blocks (a segmenter, a switcher, and multiple recognizers) and the reinforcement learning of the segmenter: The segmenter partitions a given word image into multiple character images, the switcher assigns a recognizer for each sub-image, and the recognizers perform the recognition of assigned sub-images. The training of recognizers and switcher can be considered traditional image classification tasks and we train them with a supervised learning method. However, the supervised learning of the segmenter has two critical drawbacks: Its objective function is sub-optimal and its training requires a large amount of annotation efforts. Thus, by adopting the REINFORCE algorithm, we train the segmenter so as to optimize the overall performance, i.e., we minimize the edit distance of final recognition results. Experimental results have shown that the proposed method significantly improves the performance for multilingual scripts and large character set languages without using character boundary labels. INDEX TERMS Deep learning, document analysis, optical character recognition,

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“…Knowledge about the lines in an image is useful in many applications such as unmanned vehicle guidance, robot navigation, medical image processing, object recognition, computer vision and artificial intelligence [1][2][3][4][5][6][7][8][9][10][11][12]. In a high-resolution image, thousands of lines in different angles are possible.…”

Section: Introductionmentioning

confidence: 99%

A Fast-Efficient parallel processing algorithm for straight line detection

Rasiq¹,

Jeevan²,

Krishnakumar³

2019

IJEECS

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<p>This work presents a novel method for detecting straight lines in an image at a very high speed with optimum number of processors and their functionalities. The method can be used to extract straight lines directly from an image without noise removal and pre-processing. First the square image is converted to a binary edge image using a parallel edge detection mechanism. The parallel edge detection mechanism used in this work is capable of producing edge image within a short time. Then the binary square image is transferred to a system having large number of Processing Elements (PEs). A PE has only limited jobs such as pixel scanning, compare line length with nearby PEs and transmit data to the Main Control Unit (MCU). The MCU collects data from all PEs and evaluates straight lines. Even if the number of PEs is high, it is comparatively very much less than the parallel Hough Transform method and practically implementable using recent ULSI technologies.</p>

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Robust skew estimation using straight lines in document images

Cited by 6 publications

References 25 publications

Investigating coupling preprocessing with shallow and deep convolutional neural networks in document image classification

Investigating coupling preprocessing with shallow and deep convolutional neural networks in document image classification

Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter

A Fast-Efficient parallel processing algorithm for straight line detection

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