Entity Extraction and Correction Based on Token Structure Model Generation
The logical and semantic structure analysis is a basic process for invoice understanding. Be able to carry out a robust layout analysis is very difficult due to highly heterogeneous invoice templates. In this paper, we propose a local structure for entity extraction and correction from scanned invoices. It attempts to extract entity in contiguous and noncontiguous structure by automatic finding the local structure of each entity without structure model matching and user intervention. Firstly, the entities are labeled in OCRed invoice. Combining labeled entities with geometric and semantic relations, token structure models are generated. These models are used for entity extraction and mislabeling correction by ignoring some superfluous tokens detected by labeling step. The correction module to the contiguous structure differs from that of the noncontiguous structure. The obtained results with a dataset of real invoices are reported in experimental section.
KeywordsContextual search Contiguous and noncontiguous structure Mislabeling correction Token structure models
We are grateful to CPG Company for providing real invoices for test.
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