Automatic detection and recognition of Korean text in outdoor signboard images

Park, Jong‐Hyun; Lee, Guee-Sang; Kim, Euichul; Lim, Jinsub; Kim, Soo-Hyung; Yang, Hyung-Jeong; Lee, Myunghun; Hwang, Seongtaek

doi:10.1016/j.patrec.2010.05.024

Cited by 44 publications

(25 citation statements)

References 15 publications

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“…In case of scene text (Challenge 2), features based on gradient information of connected components are also taken into consideration during validation. 4) "KAIST AIPR System": 6 Our algorithm for text localization task is not yet published. The description is as follows.…”

Section: ) "Yi's Method"mentioning

confidence: 99%

“…Research on scene text detection and recognition already started in mid-90s [1], [2] and since then has seen a substantial amount of growth with a large number of approaches published in the last decade [3], [4], [5], [6], [7]. While approaches for scene text detection/recognition tackle a much broader set of problems (non-planar surfaces, unkown layout, blur, varying distance to camera, much broader resolution ranges), synergies exist with other methods handling a subset of these problems.…”

Section: Introductionmentioning

confidence: 99%

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ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Shahab

Shafait

Dengel

2011

2011 International Conference on Document Analysis and Recognition

352

202

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Abstract-Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance.

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Section: ) "Yi's Method"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Shahab

Shafait

Dengel

2011

2011 International Conference on Document Analysis and Recognition

352

202

View full text Add to dashboard Cite

show abstract

“…Park et al [21] used vertical and horizontal projection visibility to detect Korean text in out-of-door signboards. Shiva kumara et al [22] proposed the classification of low and high contract images for detection of text.…”

Section: Region Based Methodsmentioning

confidence: 99%

“…A review of the new advancements achieved in the text recognition in multimedia documents is presented in [36]. Various issues related to the recognition problem have been identified, including text detection [9,21,23,46], Enhancement of Text and linearization [3,15,24,20, 48] (a pre-processing step that aims to improve recognition performance), character segmentation [2,22,31,34], and its recognition [4,17,32] and the integration of linguistic knowledge [11,27,49].…”

Section: Related Workmentioning

confidence: 99%

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Automated Recognition of Text in Images: A Survey

Wadhawan¹,

Gajendran²

2015

IJCA

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OCR (Optical Character Recognition) System works in the domain of Natural Language Processing and Image Processing. This is used to convert all the text information that is present in image form, to text format. Text is one of the most influential inventions of Humanity. The fertile and precise information incorporated in text is very useful in a wide range of applications that are computer-vision based, and hence text detection and recognition in natural scenes (e.g.: traffic sign boards, license plate, Hoardings and videos etc.) have become important and active research topics in computer vision and document analysis. This survey paper presents a review of various state-of-the-art techniques proposed for different processes (i.e. detection, localization, extraction, etc.) of text information processing in Images. Literature review can further serve as a good reference for researchers in the areas of scene text detection and recognition. The aim is to introduce the researchers to the latest trends in this area and to serve as a resource for developers who wish to integrate such solutions into their own work.

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Recognition of Limited Vocabulary Kannada Words Through Structural Pattern Matching: An Experimentation on Low Resolution Images

Angadi¹,

Kodabagi²

2013

Lecture Notes in Electrical Engineering

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Automatic detection and recognition of Korean text in outdoor signboard images

Cited by 44 publications

References 15 publications

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Automated Recognition of Text in Images: A Survey

Recognition of Limited Vocabulary Kannada Words Through Structural Pattern Matching: An Experimentation on Low Resolution Images

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