Receipt automatic reader

Abstract

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.

Digitizing Receipts with OCR

“…Although an OCR can be built from scratch [8], the simpler and more practical approach is by using a pre-trained OCR engine. Google Cloud Vision (GCV) [9], [10], [11], [12] and Tesseract [13], [14], [15], [16] are two of the most prominent OCR engines implemented on receipts. Tesseract is a free open source engine whereas GCV provides a limited number of calls for free.…”