Writer identification using global wavelet-based features
Neurocomputing
Retrieval of online handwriting by synthesis and matching
Pattern Recognition
Versatile search of scanned Arabic handwriting
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Text image spotting using local crowdedness and hausdorff distance
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
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Document storage and retrieval capabilities of the CEDAR-FOX forensic handwritten document examination system are described. The system is designed for automated and semi-automated analysis of scanned handwritten documents. For library creation, the systemprovides functionalities for (i) entering document metadata, e.g., identification number, writer and other collateral information, (ii) creating a textual transcript of the image content at the word level, and (iii) including automatically extracted document level features, e.g, stroke width, slant, word gaps, as well as finer features that capture the structural characteristics of characters and words. For extracting these features the system performs page analysis, page segmentation, line separation, word segmentation and finally recognition of characters and words. The extracted features are used for writer identification by matching against a library built as a database. The system design is driven by questioned document examination with its emphasis on writer identification. Several query modalities are permitted for retrieval: (i) document level: the entire document image is the query; (ii) partial image: a region of interest (ROI) of a document; (ii) word image: which is also called word spotting; (iv) text keyword: the user can type in keywords ranging from the words in the documents, case number, person names, time and the pre-registered keywords such as brief descriptions of the case. The system has been implemented using Microsoft visual C++ and tested using MySQL database system from MySQL ABTM. It provides as a graphical user interface for forensic documentidentification, verification and analysis.