CloudScan - A configuration-free invoice analysis system using recurrent neural networks
TABLE III: Expected performance on next invoice from unseen template. Best results in bold.
['Field', 'F1 Baseline', 'F1 LSTM', 'Precision Baseline', 'Precision LSTM', 'Recall Baseline', 'Recall LSTM']
This measures the generalization performance of the system across templates which is a much harder task due to the plurality of invoice layouts and reflects the experience a new user will have the first time they use the system. On this harder task the LSTM model clearly outperform the baseline system with an average F1 of 0.840 compared to 0.788. Notably the 0.840 average F1 of the LSTM model is getting close to the 0.891 average F1 of experiment 1, indicating that the LSTM model is largely learning a template invariant model of invoices , i.e. it is picking up on general patterns rather than just memorizing specific templates.