Deep Learning Approach for Receipt Recognition

TL;DR

This paper introduces a deep learning system for recognizing scanned receipts, combining text detection and recognition modules, with pre-processing and OCR verification to improve accuracy, achieving a 71.9% F1 score.

Contribution

It presents a novel deep learning approach with integrated pre-processing and verification steps for improved receipt recognition accuracy.

Findings

Achieved 71.9% F1 score in detection and recognition

Pre-processing and OCR verification improved accuracy

System effectively handles receipt images with handwriting

Abstract

Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed pre-processing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task.