EFFICIENT UNCONSTRAINED STROKE DETECTOR

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00366695" target="_blank" >RIV/68407700:21230/23:00366695 - isvavai.cz</a>

Výsledek na webu

<a href="https://worldwide.espacenet.com/patent/search/family/054979874/publication/EP3380990B1?q=EP3380990B1" target="_blank" >https://worldwide.espacenet.com/patent/search/family/054979874/publication/EP3380990B1?q=EP3380990B1</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

EFFICIENT UNCONSTRAINED STROKE DETECTOR

Popis výsledku v původním jazyce

Scene text localization and recognition, a.k.a. the text-in-the-wild problem, is a key component of many applications such as automated translation, image/video database indexing, assistance to the visually impaired, etc. So far, unlike printed document OCR, no method has reached sufficient accuracy and speed for a practical exploitation. It is, therefore, a need in the art for a fast and accurate text localization and recognition. Stroke detection has applications in extraction of structure information from images, such as bar codes. We propose a novel easy-to-implement stroke detector which is significantly faster and produces significantly less false detections than the detectors commonly used by scene text localization methods. Following the observation that text in virtually any script is formed of strokes, stroke keypoints are efficiently detected, see Figure 1, and then exploited to obtain stroke segmentations. We also propose an efficient classification step to eliminate segmentations which do not correspond to text fragments. Text fragment can be a single character, a group of characters, a whole word or a part of a character. The classifier exploits features already calculated in the detection phase and an effectively approximated "strokeness" feature, which plays an important role in the discrimination between text fragments and a background clutter. Last but not least, an efficient text clustering algorithm based on text direction voting is proposed, in order to aggregate detected segmentations into text line structures and to allow processing by subsequent stages, e.g. an OCR module. When the proposed detector is plugged into a scene text localization and recognition pipeline, the state-of-theart text localization results are maintained whilst the processing time is significantly reduced. The proposed detector has high application potential e.g. in real-time scene text detection in a video stream on mobile phones and embedded systems since a straightforward, single-thread non-optimized version of the algorithm runs in near real-time. Since all stages are scale and rotation invariant and they are not script-specific, a wide variety of fonts and scripts such as Latin, Hebrew, and Chinese can be detected. The detector can be used to produce repeatable local features for e.g. image matching and retrieval.

Název v anglickém jazyce

EFFICIENT UNCONSTRAINED STROKE DETECTOR

Popis výsledku anglicky

Scene text localization and recognition, a.k.a. the text-in-the-wild problem, is a key component of many applications such as automated translation, image/video database indexing, assistance to the visually impaired, etc. So far, unlike printed document OCR, no method has reached sufficient accuracy and speed for a practical exploitation. It is, therefore, a need in the art for a fast and accurate text localization and recognition. Stroke detection has applications in extraction of structure information from images, such as bar codes. We propose a novel easy-to-implement stroke detector which is significantly faster and produces significantly less false detections than the detectors commonly used by scene text localization methods. Following the observation that text in virtually any script is formed of strokes, stroke keypoints are efficiently detected, see Figure 1, and then exploited to obtain stroke segmentations. We also propose an efficient classification step to eliminate segmentations which do not correspond to text fragments. Text fragment can be a single character, a group of characters, a whole word or a part of a character. The classifier exploits features already calculated in the detection phase and an effectively approximated "strokeness" feature, which plays an important role in the discrimination between text fragments and a background clutter. Last but not least, an efficient text clustering algorithm based on text direction voting is proposed, in order to aggregate detected segmentations into text line structures and to allow processing by subsequent stages, e.g. an OCR module. When the proposed detector is plugged into a scene text localization and recognition pipeline, the state-of-theart text localization results are maintained whilst the processing time is significantly reduced. The proposed detector has high application potential e.g. in real-time scene text detection in a video stream on mobile phones and embedded systems since a straightforward, single-thread non-optimized version of the algorithm runs in near real-time. Since all stages are scale and rotation invariant and they are not script-specific, a wide variety of fonts and scripts such as Latin, Hebrew, and Chinese can be detected. The detector can be used to produce repeatable local features for e.g. image matching and retrieval.

Druh

P - Patent

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Číslo patentu nebo vzoru

EP3380990

Vydavatel

EPO_1 -

Název vydavatele

European Patent Office

Místo vydání

Munich, The Hague, Berlin, Vienna, Brussels

Stát vydání

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Datum přijetí

7. 6. 2023

Název vlastníka

České vysoké učení technické v Praze

Způsob využití

A - Výsledek využívá pouze poskytovatel

Druh možnosti využití

A - K využití výsledku jiným subjektem je vždy nutné nabytí licence