International Journal of Computer Vision
High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The indexing and retrieval of document images: a survey
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Searching Off-line Arabic Documents
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Retrieval of Ottoman documents
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Matching ottoman words: an image retrieval approach to historical document indexing
Proceedings of the 6th ACM international conference on Image and video retrieval
TinyLex: static n-gram index pruning with perfect recall
Proceedings of the 17th ACM conference on Information and knowledge management
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Content-based retrieval of historical Ottoman documents stored as textual images
IEEE Transactions on Image Processing
Translating handwritten bushman texts
Proceedings of the 10th annual joint conference on Digital libraries
A line-based representation for matching words in historical manuscripts
Pattern Recognition Letters
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This article presents Ottoman Archives Explorer, a Content-Based Retrieval (CBR) system based on character recognition for printed and handwritten historical documents. Several methods for character segmentation and recognition stages are investigated. In particular, sliding-window and histogram segmentation methods are coupled with recognition approaches using spatial features, neural networks, and a graph-based model. The prototype system provides CBR of document images using both example-based queries and a virtual keyboard to construct query words.