Thematic alignment of recorded speech with documents
Proceedings of the 2003 ACM symposium on Document engineering
Logical Labeling of Arabic Newspapers using Artificial Neural Nets
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Automatic categorization of figures in scientific documents
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Structure-preserving pipelines for digital libraries
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
A novel figure panel classification and extraction method for document image understanding
International Journal of Data Mining and Bioinformatics
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The analysis of a document image to derive a symbolic description of its structure and contents involves using spatial domain knowledge to classify the different printed blocks (e.g., text paragraphs), group them into logical units (e.g., newspaper stories), and determine the reading order of the text blocks within each unit. These steps describe the conversion of the physical structure of a document into its logical structure. We have developed a computational model for document logical structure derivation, in which a rule-based control strategy utilizes the data obtained from analyzing a digitized document image, and makes inferences using a multi-level knowledge base of document layout rules. The knowledge-based document logical structure derivation system (DeLoS) based on this model consists of a hierarchical rule-based control system to guide the block classification, grouping and read-ordering operations; a global data structure to store the document image data and incremental inferences; and a domain knowledge base to encode the rules governing document layout.