Scale Space Technique for Word Segmentation in Handwritten Documents
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
A Hybrid Approach t Word Segmentation
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Robust Line Detection in Historical Church Registers
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Gap metrics for word separation in handwritten lines
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Word Segmentation in Handwritten Korean Text Lines Based on Gap Clustering Techniques
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Line Detection and Segmentation in Historical Church Registers
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Baseline Image Classification Approach Using Local Minima Selection
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
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Handwriting recognition requires a prior segmentation of text lines which is a challenging task, especially for historical scripts. Exemplary for the date in entries of historical church registers, we present an approach which enables a segmentation by using additional knowledge about the word sequence. The algorithm is based on probability distribution curves and a neural network, which assesses local features of potential word boundaries. Our database consists of 298 different date entries from the 18th and 19th century which contain 674 word boundaries. The algorithm generates hypotheses for the expected date type, ordered by their probability. Tests resulted in an accuracy of 97% for the best four hypotheses.