Locating text in complex color images

Zhong, Yu; Karu, Kalle; Jain, Anil K.

doi:10.1109/icdar.1995.598963

Cited by 94 publications

(99 citation statements)

References 7 publications

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“…In addition, these methods mostly handle horizontal texts and are sensitive to rotation and scale change. In an early work, Zhong et al [35] proposed a method for text localization in color images. Horizontal spatial variance was utilized to roughly localize texts and then color segmentation was performed within the localized regions to find texts.…”

Section: Related Work On Scene Text Detectionmentioning

confidence: 99%

“…There are mainly three types of methods: texture based methods, component based methods and hybrid methods. [8,[35][36][37] treat texts as a special type of texture and make use of their textural properties, such as local intensities, filter responses and wavelet coefficients, to distinguish between text and non text areas in the images. These methods are usually computationally expensive as all locations and scales should be scanned.…”

Section: Related Work On Scene Text Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

356

158

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Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.

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Section: Related Work On Scene Text Detectionmentioning

confidence: 99%

Section: Related Work On Scene Text Detectionmentioning

confidence: 99%

Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

356

158

View full text Add to dashboard Cite

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“…Zhong et al propose a simple method to locate text in complex images [29]. Their first approach is mainly based on finding connected monochrome color regions of certain size, while the second locates text based on its specific spatial variance.…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al report a recognition rate of 84% for 35 images. However, as with the approach of Zhong et al [29], the algorithms were designed to deal with scanned images. Therefore, they cannot be applied directly to video frames.…”

Section: Related Workmentioning

confidence: 99%

Automatic text segmentation and text recognition for video indexing

2000

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Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our new methods for automatic segmentation of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. The unique features of our approach are the tracking of characters and words over their complete duration of occurrence in a video and the integration of the multiple bitmaps of a character over time into a single bitmap. The output of the text segmentation step is then directly passed to a standard OCR software package in order to translate the segmented text into ASCII. Also, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with existing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher level semantics in videos.

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“…The fact has inspired many efforts on text recognition in static images, as well as video sequences [7][8][9][10][11][12]. In [13,14], Yang et al develop a machine translation system which automatically detects and recognizes texts in natural scenes.…”

Section: Introductionmentioning

confidence: 99%