Word Spotting: A New Approach to Indexing Handwriting
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Word Spotting in Chinese Document Images without Layout Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Features for Word Spotting in Historical Manuscripts
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Journal of Cognitive Neuroscience
Font Adaptive Word Indexing of Modern Printed Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Orientation Extraction for Wordspotting in Syriac Manuscripts
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Towards an omnilingual word retrieval system for ancient manuscripts
Pattern Recognition
Handwritten word-spotting using hidden Markov models and universal vocabularies
Pattern Recognition
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting
Pattern Recognition Letters
Indexation of Syriac manuscripts using directional features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image processing for historical newspaper archives
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Automatic keyword extraction from historical document images
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Synthesizing queries for handwritten word image retrieval
Pattern Recognition
Word spotting application in historical mongolian document images
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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A new method for text retrieval that does not need segmentation is described. Segmenting the images in historical documents into individual characters is difficult. Therefore, the conventional OCR method, which uses segmentation, does not work well. Our method instead divides the text image into a sequence of small slits. The image region that corresponds to the query image region is retrieved by solving the matching problem of these sequences. Applying the eigenspace method to the slit images enables us to solve the matching problem efficiently. Moreover, using dynamic time warping (DTW) further improves the results. Our method has higher accuracy than the simple template matching method, and it has far higher efficiency in computational cost.