Name
Abstract | ||
|---|---|---|
One of the issues in extracting natural language sentences from PDF documents is the identification of non-textual elements in a sentence. In this paper, we report our preliminary results on the identification of in-line mathematical expressions. We first construct a manually annotated corpus and apply conditional random field (CRF) for the math-zone identification using both layout features, such as font types, and linguistic features, such as context n-grams, obtained from PDF documents. Although our method is naive and uses a small amount of annotated training data, our method achieved an 88.95% F-measure compared with 22.81% for existing math OCR software.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3103010.3121041 | DocEng |
Keywords | Field | DocType |
PDF structure analysis, mathematical formula recognition, in-line mathematical expression detection, math IR, scientific paper mining | Training set,Conditional random field,Information retrieval,Expression (mathematics),Computer science,Software,Natural language,Natural language processing,Artificial intelligence,Sentence,Database,Typeface | Conference |
ISBN | Citations | PageRank |
978-1-4503-4689-4 | 3 | 0.41 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenichi Iwatsuki | 1 | 3 | 2.10 |
| Takeshi Sagara | 2 | 3 | 0.41 |
| Tadayoshi Hara | 3 | 118 | 9.54 |
| Akiko N. Aizawa | 4 | 678 | 120.63 |