Real-Time Text Localization in Natural Scene Images Using a Linear Spatial Filter

Gironés, Xavier; Julià, Carme

doi:10.1109/icdar.2017.208

Cited by 6 publications

(14 citation statements)

References 35 publications

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“…In this paper, we prefer to employ the box filtering method Eq. (10), which is considered one of the most accurate methods for stroke detection [19] and is competitive with recursive filters [21,22]. The box filtering method sums up the averaging filtering to approximate a Gaussian filter ĝ(x, y| ) , as noted in Eq.…”

Section: Fast Log Filteringmentioning

confidence: 99%

“…The local operator extracts candidate keypoints at the locations of text elements in an image. Different timeefficient operators have been proposed in the literature for scene text detection, such as the FASText [7], the Canny Text [8], the Stroke Width Transform (SWT) [9], and the BSV [10]. However, most of time-efficient methods are dominated by the Maximally Stable Extremal Regions (MSER) operator [11,12].…”

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confidence: 99%

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Fast RT-LoG operator for scene text detection

Dinh

Delalandre

Conte

2020

J Real-Time Image Proc

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Fast Log Filteringmentioning

confidence: 99%

mentioning

confidence: 99%

Fast RT-LoG operator for scene text detection

Dinh

Delalandre

Conte

2020

J Real-Time Image Proc

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“…The real-time systems in the [1] [3], [4] [6], [7], [8], [9], [10] apply the strategy of two stages composing of detection and recognition. The detection localizes the text components at a low complexity level and groups them into text candidate regions before classification.…”

Section: Introductionmentioning

confidence: 99%

“…The recent works on the topic drive the text processing as a blob detection problem with the maximally stable extremal regions (MSER) [3], [5] and the LoG-based operators [6], [8], [10], [4], [12]. MSER looks for the local intensity extrema and applies a watershed-like segmentation algorithm for detection.…”

Section: Introductionmentioning

confidence: 99%

THẢO LUẬN VỀ CÁC TOÁN TỬ DỰA TRÊN LoG ĐỂ PHÁT HIỆN VĂN BẢN THEO THỜI GIAN THỰC

Nguyễn¹

2021

SJ TTU

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Trong bài báo này trình bày các phương pháp phát hiện văn bản thời gian thực trong hình ảnh dựa trên máy ảnh, tập trung đặc biệt vào toán tử Laplacian of Gaussian (LoG). Các phương pháp này được thảo luận với sự tập trung cụ thể vào các khía cạnh của tính phức tạp và tính mạnh mẽ. Một số kết quả minh họa và các thí nghiệm cơ bản được đưa ra để mô tả đặc điểm của các phương pháp. Hơn nữa, bài báo cũng cung cấp nhận xét về những cải tiến của các phương pháp đối với vấn đề phát hiện văn bản.

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“…The input images are preprocessed by MSER and deep convolution neural network is used for classification to predict text or non-text regions. Linear spatial filter based text localization is described in [11]. The input natural scene images are preprocessed and extracted by multi-scale pyramid, connected components and spatial filtering.…”

Section: Introductionmentioning

confidence: 99%

Text Localization in Scene Images by Bendelet Transform

Gupta¹,

K²

2019

International Journal of Advances in Signal and Image Sciences

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In an automated text recognition system, one of the prerequisites is the localization of text. It is a challenging task in scene image due to their background and non uniform size of characters in the images. In this study, an efficient text localization system using bendlet transform is presented. Among the various multi-resolution and multi-directional analysis, bendlet transform has superior property that they classify the curvature precisely. To achieve this property, it uses an addition parameter than shearlets called bending operator. The system decomposes the scene images by bendlet transform and then reconstructs using the bands which contains only the edge information. Then, a series of post processing is applied to locate the text region in a scene image. Results show the robustness of the text localization system by successfully locating the text region in the scene images with different background and non-uniform text sizes.

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Real-Time Text Localization in Natural Scene Images Using a Linear Spatial Filter

Cited by 6 publications

References 35 publications

Fast RT-LoG operator for scene text detection

Fast RT-LoG operator for scene text detection

THẢO LUẬN VỀ CÁC TOÁN TỬ DỰA TRÊN LoG ĐỂ PHÁT HIỆN VĂN BẢN THEO THỜI GIAN THỰC

Text Localization in Scene Images by Bendelet Transform

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