Detecting text in natural scene images with conditional clustering and convolution neural network

Anna Zhu, Guoyou Wang, Yangbo Dong, Brian Kenji Iwana

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We present a robust method of detecting text in natural scenes. The work consists of four parts. First, automatically partition the images into different layers based on conditional clustering. The clustering operates in two sequential ways. One has a constrained clustering center and conditional determined cluster numbers, which generate small-size subregions. The other has fixed cluster numbers, which generate full-size subregions. After the clustering, we obtain a bunch of connected components (CCs) in each subregion. In the second step, the convolutional neural network (CNN) is used to classify those CCs to character components or noncharacter ones. The output score of the CNN can be transferred to the postprobability of characters. Then we group the candidate characters into text strings based on the probability and location. Finally, we use a verification step. We choose a multichannel strategy to evaluate the performance on the public datasets: ICDAR2011 and ICDAR2013. The experimental results demonstrate that our algorithm achieves a superior performance compared with the state-of-the-art text detection algorithms.

Original languageEnglish
Article number053019
JournalJournal of Electronic Imaging
Volume24
Issue number5
DOIs
Publication statusPublished - Sep 1 2015

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

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