Marco DeGemmis


2022

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swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain
Pierluigi Cassotti | Cataldo Musto | Marco DeGemmis | Georgios Lekkas | Giovanni Semeraro
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.