Enhancing the Ensemble-Based Scene Character Recognition by Using Classification Likelihood

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Research on scene character recognition has been popular for its potential in many applications including automatic translator, signboard recognition, and reading assistant for the visually-impaired. The scene character recognition is challenging and difficult owing to various environmental factors at image capturing and complex design of characters. Current OCR systems have not gained practical accuracy for arbitrary scene characters, although some effective methods were proposed in the past. In order to enhance existing recognition systems, we propose a hierarchical recognition method utilizing the classification likelihood and image pre-processing methods. It is shown that the accuracy of our latest ensemble system has been improved from 80.7% to 82.3% by adopting the proposed methods.

Original languageEnglish
Title of host publicationPattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Number of pages11
ISBN (Print)9783030412982
Publication statusPublished - 2020
Event5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand
Duration: 2019 Nov 262019 Nov 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12047 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th Asian Conference on Pattern Recognition, ACPR 2019
Country/TerritoryNew Zealand


  • Ensemble voting classifier
  • Hierarchical recognition method
  • Synthetic Scene Character Data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Enhancing the Ensemble-Based Scene Character Recognition by Using Classification Likelihood'. Together they form a unique fingerprint.

Cite this