Segmentation of the Date in Entries of Historical Church Registers
Proceedings of the 24th DAGM Symposium on Pattern Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Text Extraction from Gray Scale Historical Document Images Using Adaptive Local Connectivity Map
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Structuralizing digital ink for efficient selection
Proceedings of the 11th international conference on Intelligent user interfaces
A chaincode based scheme for fingerprint feature extraction
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A method for combining complementary techniques for document image segmentation
Pattern Recognition
A method for combining complementary techniques for document image segmentation
Pattern Recognition
Handwritten document image segmentation into text lines and words
Pattern Recognition
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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Abstract: For being able to automatically acquire the information recorded in church registers and other historical scriptures, the writing on these documents has to be recognized. This paper describes algorithms for transforming the paper documents into a representation of text apt to be used as input for an automatic text recognizer. The automatic recognition of old handwritten scriptures is difficult for two main reasons. Lines of text in general are not straight and ascenders and descenders of adjacent lines interfere. The algorithms described in this paper provide ways to reconstruct the path of the lines of text using an approach of gradually constructing line segments until an unique line of text is formed. In addition, the single lines are segmented and an output in form of a raster image is provided. The method was applied to church registers. They were written between the 17th and 19th century. Line segmentation was found to be successful in 97% of all samples.