ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019

Nayef, Nibal; Liu, Cheng‐Lin; Ogier, Jean-Marc; Patel, Yash; Bušta, Michal; Chowdhury, Pinaki Nath; Karatzas, Dìmosthenis; Khlif, Wafa; Matas, Jiřı́; Pal, Umapada; Burie, Jean-Christophe

doi:10.1109/icdar.2019.00254

Cited by 227 publications

(109 citation statements)

References 18 publications

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“…By using a simple polygon non-maximum suppression over the entire detected text instances, we obtained the final text locations. The experimental result is promising in terms of detection accuracy on the standard test benchmarks, including ICDAR2015 [12], ICDAR2017-MLT [13], ICDAR2019-MLT [14], and Total-Text [15].…”

Section: Introductionmentioning

confidence: 90%

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Scene Text Detection with Polygon Offsetting and Border Augmentation

2020

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Scene text localization is a very crucial step in the issue of scene text recognition. The major challenges—such as how there are various sizes, shapes, unpredictable orientations, a wide range of colors and styles, occlusion, and local and global illumination variations—make the problem different from generic object detection. Unlike existing scene text localization methods, here we present a segmentation-based text detector which can detect an arbitrary shaped scene text by using polygon offsetting, combined with the border augmentation. This technique better distinguishes contiguous and arbitrary shaped text instances from nearby non-text regions. The quantitative experimental results on public benchmarks, ICDAR2015, ICDAR2017-MLT, ICDAR2019-MLT, and Total-Text datasets demonstrate the performance and robustness of our proposed method, compared to previous approaches which have been proposed.

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Section: Introductionmentioning

confidence: 90%

“…ICDAR2019-MLT [14] is the latest multi-lingual scene text dataset. This real-world dataset consisted of 10,000 training and 10,000 testing images containing text from 10 languages.…”

Section: Datasetsmentioning

confidence: 99%

Scene Text Detection with Polygon Offsetting and Border Augmentation

2020

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“…One such effort is [220]. In another attempt, the research community has launched "ICDAR 2019: Robustreading challenge on multilingual scene text detection and recognition" [221]. Aim of this challenge invites research studies that propose a robust system for multi-lingual text recognition in daily life or "in the wild" scenario.…”

Section: B Future Workmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

360

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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“…c) MLT 2019: This dataset [6] contains 10, 000 training images and 10, 000 testing images containing scene images with text in 10 languages, 1, 000 images per language, including Arabic, Bangla, Chinese, Devanagari, English, French, German, Italian, Japanese and Korean.…”

Section: A Datasetsmentioning

confidence: 99%

Multi-Lingual Text Localization via Language-Specific Convolutional Neural Networks

Conceição

Pinto

Decker

et al. 2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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Scene text localization and recognition is a topic in computer vision that aims to delimit candidate regions in an input image containing incidental scene text elements. The challenge of this research consists in devising detectors capable of dealing with a wide range of variability, such as font size, font style, color, complex background, text in different languages, among others. This work presents a comparison between two strategies of building classification models, based on a Convolution Neural Network method, to detect textual elements in multiple languages in images: (i) classification model built on a multi-lingual training scenario; and (ii) classification model built on a language-specific training scenario. The experiments designed in this work indicate that language-specific model outperforms the classification model trained over a multi-lingual scenario, with an improvement of 14.79%, 8.94%, and 11.43%, in terms of precision, recall, and F-measure values, respectively.

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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019

Cited by 227 publications

References 18 publications

Scene Text Detection with Polygon Offsetting and Border Augmentation

Scene Text Detection with Polygon Offsetting and Border Augmentation

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Multi-Lingual Text Localization via Language-Specific Convolutional Neural Networks

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