Segmentation of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Imaged Document Text Retrieval Without OCR
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
A New Scheme for Off-Line Handwritten Connected Digit Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Integrated segmentation and recognition of handwritten numeralswith cascade neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hierarchical morphological segmentation for image sequence coding
IEEE Transactions on Image Processing
Improved techniques for automatic image segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Identifying the writer of ancient inscriptions and Byzantine codices. A novel approach
Computer Vision and Image Understanding
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This work proposes a novel adaptive approach for character segmentation and feature vector extraction from seriously degraded images. An algorithm based on the histogram automatically detects fragments and merges these fragments before segmenting the fragmented characters. A morphological thickening algorithm automatically locates reference lines for separating the overlapped characters. A morphological thinning algorithm and the segmentation cost calculation automatically determine the baseline for segmenting the connected characters. Basically, our approach can detect fragmented, overlapped, or connected character and adaptively apply for one of three algorithms without manual fine-tuning. Seriously degraded images as license plate images taken from real world are used in the experiments to evaluate the robustness, the flexibility and the effectiveness of our approach. The system approach output data as feature vectors keep useful information more accurately to be used as input data in an automatic pattern recognition system.