Automatic Recognition of Units of Measurement in Product Descriptions from Tax Invoices Using Neural Networks

Lucas Fernandes Lucena, Telmo de Menezes e Silva Filho, Thaís Gaudencio do Rêgo, Yuri Malheiros

Abstract: Tax evasion is a problem that affects our society, costing billions of Brazilian reais of public funds every year. Stopping this practice is a complex challenge that involves analyzing a large and diverse volume of data. In this work, we propose an approach to analyze invoices and extract information about measures and units from product descriptions using a neural network with the BiLSTM-CRF architecture. Our method can validate product quantity information to, for instance, check whether any product was bought or sold by a business without issuing an invoice. The results were evaluated according to precision, recall, and f-score. The proposed approach can correctly detect more than 90% of cases of each type of information, showing its feasibility to process invoice data.