Improved optical character recognition with deep neural network

Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. In old or poorly printed documents, printed characters are typically broken and blurred, making character recognition potentially far m...

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) pp. 245 - 249
Main Authors Wei, Tan Chiang, Sheikh, U. U., Rahman, Ab Al-Hadi Ab
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Character recognition
Deep Learning
Image segmentation
Neural networks
OCR (Optical Character Recognition)
Optical character recognition software
Optical imaging
Optical signal processing
Training
Transfer Learning
Online AccessGet full text

Cover

More Information
Summary:Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. In old or poorly printed documents, printed characters are typically broken and blurred, making character recognition potentially far more complex. In this work, deep neural network using Inception V3 is used to train and perform OCR. The Inception V3 network is trained with 53,342 noisy character images, which were collected from receipts and newspapers. Our experimental results show that the proposed deep neural network achieved significantly better recognition accuracy on poor quality text images and resulted in an overall 21.5% reduction in error rate compared to existing OCRs.
DOI:10.1109/CSPA.2018.8368720