video_text_detection
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Arxa
Bachelor Thesis | Text extraction from complex video scenes
Department of Engineering Informatics
Thesis Title
Text extraction from complex video scenesSupervisor
Dr. Athanasios Nikolaidis, [email protected]Objective
The objective of my Thesis was the development of an Image Processing desktop application, capable of detecting and extracting text displays from videos with complex backgrounds. My approach implements Machine Learning and Image Analysis methodologies from various popular scientific papers.Technologies
JavaFX, OpenCV, LIBSVM, Gradle, JUnit, TestFXReferences
[1] Palaiahnakote Shivakumara, Trung Quy Phan and Chew Lim Tan, Senior Member, IEEE “A Laplacian Approach to Multi-Oriented Text Detection in Video”, IEEE
[2] Trung Quy Phan, Palaiahnakote Shivakumara and Chew Lim Tan “A Laplacian Method for Video Text Detection”, School of Computing, National University of Singapore, 2009
[3] Rakesh Mehta, Karen Egiazarian, “Rotated Local Binary Pattern (RLBP) Rotation invariant texture descriptor”, 2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013, Barcelona, Spain, 2013
[4] Cong Yao, “MSRA Text Detection 500 Database (MSRA-TD500)”, Huazhong University of Science and Technology, 2012
[5] Visual Geometry Group, “Synthetic Word Dataset ”, Department of Engineering Science, University of Oxford
Download my Thesis [PDF-Greek]
Text Detection
Original Frame
Gaussian Filter
Grayscale
Laplacian Filter
Maximum Gradient Difference
Binarization
Dilation
Connected Components
1st Filter - Removing components with bigger height than width
2nd Filter - Removing components with small area
3rd Filter - Classifying text areas using Support Vector Machines
Text Extraction
Cropped Text Area
Grayscale
Unsharp Masking
Otsu Binarization
Apply OCR
JavaFX Application
Main View
Settings
Choosing video file
Text Extraction
Text Detection
Arxa