Recognizing Text with Perspective Distortion in Natural Scenes

Phan, Trung Quy; Shivakumara, Palaiahnakote; Tian, Shangxuan; Tan, Chew Lim

doi:10.1109/iccv.2013.76

Cited by 314 publications

(124 citation statements)

References 16 publications

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“…In [28], a bag-of-keypoints approach is presented based on densely extracted SIFT descriptors to recognize perspective scene texts of arbitrary orientations.…”

Section: Feature Based Approachmentioning

confidence: 99%

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Tian

Bhattacharya

et al. 2016

Pattern Recognition

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134

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“…In [28], a bag-of-keypoints approach is presented based on densely extracted SIFT descriptors to recognize perspective scene texts of arbitrary orientations.…”

Section: Feature Based Approachmentioning

confidence: 99%

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Tian

Bhattacharya

et al. 2016

Pattern Recognition

Self Cite

134

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“…To tackle these challenges, a rich body of approaches have been proposed and substantial progresses have been achieved in recent years [8][9][10][11][12][13][14][15][16][17][18][19][20]. In almost all these methods, the study on representation is the main research subject, since representation is the key to the effectiveness and robustness of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

347

151

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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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“…Several works have been done in the field of text detection [3,12,16]. Similarly, many methods exist for text binarization [14,15].…”

Section: Related Workmentioning

confidence: 99%

Text recognition in scene image and video frame using Color Channel selection

Bhunia

Kumar

Roy

et al. 2017

Multimed Tools Appl

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In recent years, recognition of text from natural scene image and video frame has got increased attention among the researchers due to its various complexities and challenges. Because of low resolution, blurring effect, complex background, different fonts, color and variant alignment of text within images and video frames, etc., text recognition in such scenario is difficult. Most of the current approaches usually apply a binarization algorithm to convert them into binary images and next OCR is applied to get the recognition result. In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames. In the approach, at first, a color channel is automatically selected and then selected color channel is considered for text recognition. Our text recognition framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram of Oriented Gradient features extracted from selected color channel. From each sliding window of a color channel our color-channel selection approach analyzes the image properties from the sliding window and then a multi-label Support Vector Machine (SVM) classifier is applied to select the color channel that will provide the best recognition results in the sliding window. This color channel selection for each sliding window has been found to be more fruitful than considering a single color channel for the whole word image. Five different features have been analyzed for multi-label SVM based color channel selection where wavelet transform based feature outperforms others. Our framework of color channel selection is script-independent. It has been tested in English (Roman) and Devanagari (Indic) scripts. We have tested our approach on English datasets (ICDAR 2003, ICDAR 2013 MSRA-TD500, IIIT5K, SVT, YVT) publicly available for both video and scene images. For Devanagari script, we collected our own dataset. The performances obtained from experimental results are encouraging and show the advantage of the proposed method.

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Recognizing Text with Perspective Distortion in Natural Scenes

Cited by 314 publications

References 16 publications

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Scene text detection and recognition: recent advances and future trends

Text recognition in scene image and video frame using Color Channel selection

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