Font and function word identification in document recognition
Computer Vision and Image Understanding
Document Processing for Automatic Knowledge Acquisition
IEEE Transactions on Knowledge and Data Engineering
Logical Structure Analysis of Book Document Images Using Contents Information
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Automatic Acquisition of Layout Knowledge for Understanding Business Cards
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Automatic Knowledge Acquisition for Spatial Document Interpretation
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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SGML is a language for defining the layout structure of a document. Various attempts at generating SGML from a document image have not been successful. We focus on extracting some of the important layout elements by using flexible matching strategy and easy model generation. Our proposed approach treats each extracted element as it were independent. Some segmented areas like "title" or "author" are defined locally making the system robust, able to withstand shifting and noise. The system is also easy to operate. Since the system is not full automatic, we need to supply typical models of each component. Our GUI presents the attributes of each segmented area as well as the original bit map images. The color-coded attributes help us to easily edit the extracted component. In experiments with 288 pages of test images, the proposed method is shown to be 95.6% correct for a wide range of documents. By using 145 pages of documents as a learning set, the system recognized 99.2% of feature sets from 148 various types of unknown documents.