Deep Learning Approach for Receipt Recognition

05/30/2019
by   Anh Duc Le, et al.
0

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 task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/30/2023

DTrOCR: Decoder-only Transformer for Optical Character Recognition

Typical text recognition methods rely on an encoder-decoder structure, i...
research
08/14/2016

Stacked Approximated Regression Machine: A Simple Deep Learning Approach

With the agreement of my coauthors, I Zhangyang Wang would like to withd...
research
04/29/2019

Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms

Deep learning on an edge device requires energy efficient operation due ...
research
07/09/2019

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

We present GluonCV and GluonNLP, the deep learning toolkits for computer...
research
11/21/2014

Pre-processing of Domain Ontology Graph Generation System in Punjabi

This paper describes pre-processing phase of ontology graph generation s...
research
07/07/2021

Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches

Although abbreviations are fairly common in handwritten sources, particu...
research
12/21/2013

Extracting Region of Interest for Palm Print Authentication

Biometrics authentication is an effective method for automatically recog...

Please sign up or login with your details

Forgot password? Click here to reset