A skeleton-based method for multi-oriented video text detection
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A new wavelet-median-moment based method for multi-oriented video text detection
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
An eigen value based approach for text detection in video
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Text locating in scene images for reading and navigation aids for visually impaired persons
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
Improving computer vision-based indoor wayfinding for blind persons with context information
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
A robust video text detection approach using SVM
Expert Systems with Applications: An International Journal
Assistive text reading from complex background for blind persons
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Text extraction from scene images by character appearance and structure modeling
Computer Vision and Image Understanding
A phase-based approach for caption detection in videos
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
An approach for Bangla and Devanagari video text recognition
Proceedings of the 4th International Workshop on Multilingual OCR
| Hi-index | 0.00 |
In this paper, we propose an efficient text detection method based on the Laplacian operator. The maximum gradient difference value is computed for each pixel in the Laplacian-filtered image. K-means is then used to classify all the pixels into two clusters: text and non-text. For each candidate text region, the corresponding region in the Sobel edge map of the input image undergoes projection profile analysis to determine the boundary of the text blocks. Finally, we employ empirical rules to eliminate false positives based on geometrical properties. Experimental results show that the proposed method is able to detect text of different fonts, contrast and backgrounds. Moreover, it outperforms three existing methods in terms of detection and false positive rates.