Receipt automatic reader

Olga Maslova , Louis Klein , Damien Dabernat , A Benoit (1) , Patrick Lambert (1)

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

Smartphones bring a new way to scan and digitalize written documents by taking pictures. This enables new document analysis applications to emerge. As a counterpart, unsupervised document capturing brings new challenges mainly related to target document localization and high quality text recognition. In this context, this work addresses automatic sale receipt understanding in an industrial context. It relies on the extraction of accurate and essential consumption data even with low quality receipt captures. We propose a tool chain that combines Deep Neural Networks and traditional image processing to ensure accurate automatic data extraction. The proposed workflow is evaluated globally by the analysis of the quality of the text recognition at the end of the processing.

Mots clés: Deep Convolutional Neural Networks; Object Detection; Sale Receipt Reading; Automatic document undesrtanding

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Multimédia [cs.MM]; Réseau de neurones [cs.NE]; Machine Learning [stat.ML]

Olga Maslova, Louis Klein, Damien Dabernat, A Benoit, Patrick Lambert. Receipt automatic reader. Content-Based Multimedia Indexing (CBMI) 2019, Sep 2019, Dublin, Ireland. ⟨hal-02196644⟩