Scene Text Detection via Connected Component Clustering and Nontext Filtering

• Published in: IEEE transactions on image processing Vol. 22; no. 6; pp. 2296 - 2305
• Main Authors: Koo, Hyung Il, Kim, Duck Hoon
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2013
• Subjects: Algorithm design and analysis; Algorithms; Artificial Intelligence; CC clustering; Cluster Analysis; Clustering algorithms; connected component (CC)-based approach; Databases, Factual; Feature extraction; Image color analysis; Image Processing, Computer-Assisted - methods; machine learning classifier; nontext filtering; Partitioning algorithms; Pattern Recognition, Automated; Robustness; scene text detection; Training
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2013.2249082

In this paper, we present a new scene text detection algorithm based on two machine learning classifiers: one allows us to generate candidate word regions and the other filters out nontext ones. To be precise, we extract connected components (CCs) in images by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. Unlike conventional methods relying on heuristic rules in clustering, we train an AdaBoost classifier that determines the adjacency relationship and cluster CCs by using their pairwise relations. Then we normalize candidate word regions and determine whether each region contains text or not. Since the scale, skew, and color of each candidate can be estimated from CCs, we develop a text/nontext classifier for normalized images. This classifier is based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, we extend our approach to exploit multichannel information. Experimental results on ICDAR 2005 and 2011 robust reading competition datasets show that our method yields the state-of-the-art performance both in speed and accuracy.