Abstract
Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely ''flat'', ''ordered'', and ''divided'', giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.
| Original language | English (US) |
|---|---|
| Article number | 6628805 |
| Pages (from-to) | 1205-1209 |
| Number of pages | 5 |
| Journal | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR |
| DOIs | |
| State | Published - 2013 |
| Event | 12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States Duration: Aug 25 2013 → Aug 28 2013 |
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition