TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video OCR for Digital News Archive
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Text Detection for Video Analysis
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Robust Extraction of Text in Video
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Text Enhancement with Asymmetric Filter for Video OCR
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Multimedia descriptions based on MPEG-7: extraction and applications
IEEE Transactions on Multimedia
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
Precise news video text detection/localization based on multiple frames integration
ISCGAV'10 Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision
Robust news video text detection based on edges and line-deletion
WSEAS Transactions on Signal Processing
Hi-index | 0.01 |
In video indexing and summarization, videotext is the very compact and accurate information. Most videotext detection and extraction methods only deal with the static videotext on video frames. Few methods can handle motion videotext efficiently since motion videotext is hardly extracted well. In this paper, we propose a two-directional videotext extractor, called 2DVTE. It is developed as an integrated system to detect, localize and extract the scrolling videotexts. First, the detection method is carried out by edge information to classify regions into text and non-text regions. Second, referring to the localization on scrolling videotext, we propose the two-dimensional projection profile method with horizontal and vertical edge map information. Considering the characteristics of Chinese text, the vertical edge map is used to localize the possible text region and horizontal edge map is used to refine the text region. Third, the extraction method consists of dual mode adaptive thresholding and multi-seed filling algorithm. In the dual mode adaptive thresholding, it produces the non-rectangle pattern to divide the background and foreground more precisely. Referring to the multi-seed filling algorithm, it is based on the consideration of the minimum and maximum length and four directions of the stroke while the previous method only considers the minimum length and two directions of the stroke. With this multi-seed exploitation on strokes, precise seeds are obtained to produce more sophisticated videotext. Considering high throughput and the low complexity issue, we can achieve a real-time system on detecting, localizing, and extracting the scrolling videotexts with only one frame usage instead of multi-frame integration in other literatures. According to the experiment results on various video sequences, all of the horizontal and vertical scrolling videotexts can be extracted precisely. We also make comparisons with other methods. In our analysis, the performance of our algorithm is superior to other existing methods in speed and quality.