A Hierarchical Visual Saliency Model for Character Detection in Natural Scenes

Gao, Renwu; Uchida, Seiichi; Feng, Yaokai

doi:10.1007/978-3-319-05167-3_2

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Some of them use texture, some of them color, while some others are region based. To detect regions Gao et al [13] employ visual attention models. Even though characters could be not salient, but regions containing text are salient, therefore they apply a second pixel-based filtering after the extraction of global salient regions.…”

Section: Related Workmentioning

confidence: 99%

Camera-Based Whiteboard Reading for Understanding Mind Maps

Vajda

Plötz

Fink

2015

Int. J. Patt. Recogn. Artif. Intell.

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Mind maps, i.e. the spatial organization of ideas and concepts around a central topic and the visualization of their relations, represent a very powerful and thus popular means to support creative thinking and problem solving processes. Typically created on traditional whiteboards, they represent an important technique for collaborative brainstorming sessions. We describe a camera-based system to analyze hand-drawn mind maps written on a whiteboard. The goal of the presented system is to produce digital representations of such mind maps, which would enable digital asset management, i.e. storage and retrieval of manually created documents. Our system is based on image acquisition by means of a camera, followed by the segmentation of the particular whiteboard image focusing on the extraction of written context, i.e. the ideas captured by the mind map. The spatial arrangement of these ideas is recovered using layout analysis based on unsupervised clustering, which results in graph representations of mind maps. Finally, handwriting recognition derives textual transcripts of the ideas captured by the mind map. We demonstrate the capabilities of our mind map reading system by means of an experimental evaluation, where we analyze images of mind maps that have been drawn on whiteboards, without any further constraints other than the underlying topic. In addition to the promising recognition results, we also discuss training strategies, which effectively allow for system bootstrapping using out-of-domain sample data. The latter is important when addressing creative thinking processes where domain-related training data are difficult to obtain as they focus on novelty by definition.

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Section: Related Workmentioning

confidence: 99%

Camera-Based Whiteboard Reading for Understanding Mind Maps

Vajda

Plötz

Fink

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…Gao et al [11] have worked on detection of images in natural scene and invented a Hierarchical Visual Saliency Model. As sometimes the region containing the character is salient, and is detected by saliency based method.…”

Section: Analysis Of Existing Approachesmentioning

confidence: 99%

“…Gonzalez et al [14], [15] proposed adaptive classifier threshold method, but [15] was only designed for traffic panels. Gao et al [11] invented a visual saliency model for text detection in images and it is better than conventional Itti et al "s method [12]. Minetto et al [8] proposed snooper text in multi-scale fashion and snooper text was proved to be best than TessBack, TesseRact.…”

Section: % 71% 76%mentioning

confidence: 99%

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A Review on Text Detection Techniques

Ali¹,

Iqbal²,

Khan³

et al. 2015

VFAST trans. softw. eng.

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Text detection in image is an important field. Reading text is challenging because of the variations in images. Text detection is useful for many navigational purposes e.g. text on google API's and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these

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“…Jadderberg et al [2,17] used neural networks for text detection. Gao et al [11] invented a visual saliency model for text detection in images.…”

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confidence: 99%

A Review on Text Detection Techniques

et al. 2016

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Text detection in image is an important field. Reading text in image is challenging because of the variations in images. Text detection in images is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).

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A Hierarchical Visual Saliency Model for Character Detection in Natural Scenes

Cited by 5 publications

References 19 publications

Camera-Based Whiteboard Reading for Understanding Mind Maps

Camera-Based Whiteboard Reading for Understanding Mind Maps

A Review on Text Detection Techniques

A Review on Text Detection Techniques

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