Text detection approach based on confidence map and context information

Wang, Runmin; Sang, Nong; Gao, Changxin

doi:10.1016/j.neucom.2015.01.023

Cited by 24 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [43] text localization in natural scene images is performed by edge recombining, edge filtering and multi-channel processing sequentially. Confidence map and context information have been explored in [44] for text detection in scene images. A multistage clustering algorithm for grouping MSER components to detect multi-oriented text was proposed in [51].…”

Section: Related Workmentioning

confidence: 99%

Multi-oriented text detection and verification in video frames and scene images

et al. 2018

View full text Add to dashboard Cite

In this paper, we bring forth a novel approach of video text detection using Fourier-Laplacian filtering in the frequency domain that includes a verification technique using Hidden Markov Model (HMM). The proposed approach deals with the text region appearing not only in horizontal or vertical directions, but also in any other oblique or curved orientation in the image.Until now only a few methods have been proposed that look into curved text detection in video frames, wherein lies our novelty. In our approach, we first apply Fourier-Laplacian transform on the image followed by an ideal Laplacian-Gaussian filtering. Thereafter K-means clustering is employed to obtain the asserted text areas depending on a maximum difference map. Next, the obtained connected components (CC) are skeletonized to distinguish various text strings.Complex components are disintegrated into simpler ones according to a junction removal algorithm followed by a concatenation performed on possible combination of the disjoint skeletons to obtain the corresponding text area. Finally these text hypotheses are verified using HMM-based text/non-text classification system. False positives are thus eliminated giving us a robust text detection performance. We have tested our framework in multi-oriented text lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in video frames and scene texts.The results obtained show that proposed approach surpasses existing methods on text detection.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-oriented text detection and verification in video frames and scene images

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Though there exist many pieces of work on word spotting [13,24,25,26] in handwritten/printed text lines, not many work exist for text spotting in natural scene image/video frames. Most of the works on scene and video images are on text detection and recognition purpose [27,40,41]. It is due to low resolution, blur, background noise, etc.…”

Section: Related Workmentioning

confidence: 99%

Date-field retrieval in scene image and video frames using text enhancement and shape coding

2018

View full text Add to dashboard Cite

Text recognition in scene image and video frames is difficult because of low resolution, blur, background noise, etc. Since traditional OCRs do not perform well in such images, information retrieval using keywords could be an alternative way to index/retrieve such text information.Date is a useful piece of information which has various applications including date-wise videos/scene searching, indexing or retrieval. This paper presents a date spotting based information retrieval system for natural scene image and video frames where text appears with complex backgrounds. We propose a line based date spotting approach using Hidden Markov Model (HMM) which is used to detect the date information in a given text. Different date models are searched from a line without segmenting characters or words. Given a text line image in RGB, we apply an efficient gray image conversion to enhance the text information. Wavelet decomposition and gradient sub-bands are used to enhance text information in gray scale. Next, Pyramid Histogram of Oriented Gradient (PHOG) feature has been extracted from gray image and binary images for date-spotting framework. Binary and gray image features are combined by MLP based Tandem approach. Finally, to boost the performance further, a shape coding based scheme is used to combine the similar shape characters in same class during word spotting. For our experiment, three different date models have been constructed to search similar date information having numeric dates that contains numeral values and punctuations and seminumeric that contains dates with numerals along with months in scene/video text. We have tested our system on 1648 text lines and the results show the effectiveness of our proposed date spotting approach.

show abstract

“…The enhanced image is passed through the module of text candidates regions detection. In literature, various approaches have been employed for identifying the text candidates regions like Stroke Width Transform (SWT), Stroke Feature Transform (SFT), External Regions (ERs), Maximally Stable External Regions (MSERs) [40,49], etc. The MSER features detector is well known for its text detection robustness [44].…”

Section: The Image Processing Text Detection and Recognition Toolsmentioning

confidence: 99%

“…The individual text characters are grouped into words or phrases before proceeding to the recognition stage. It improves the performance of recognition stage and enables to identify the whole words or phrases and to transform the incoming image in meaningful text [49]. The connected component (CC)based approach is employed in order to properly group the individual text characters into words and sentences.…”

Section: The Image Processing Text Detection and Recognition Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Scene to Text Conversion and a Cymatics Based Configurable Text Perception

S¹

2018

Preprint

View full text Add to dashboard Cite

This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.

show abstract

Text detection approach based on confidence map and context information

Cited by 24 publications

References 35 publications

Multi-oriented text detection and verification in video frames and scene images

Multi-oriented text detection and verification in video frames and scene images

Date-field retrieval in scene image and video frames using text enhancement and shape coding

Scene to Text Conversion and a Cymatics Based Configurable Text Perception

Contact Info

Product

Resources

About