Robust Binarization for Video Text Recognition

  • Authors:
  • Z. Saidane;C. Garcia

  • Affiliations:
  • Orange Labs, Cesson Sevigné Cedex - France;Orange Labs, Cesson Sevigné Cedex - France

  • Venue:
  • ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
  • Year:
  • 2007

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Abstract

This paper presents an automatic binarization method for color text areas in images or videos, which is robust to complex background, low resolution or video coding arte- facts. Based on a specific architecture of convolutional neu- ral networks, the proposed system automatically learns how to perform binarization, from a training set of synthesized text images and their corresponding desired binary images, without making any assumptions or using tunable parame- ters. The proposed method is compared to state-of-the-art binarization techniques, with respect to Gaussian noise and contrast variations, demonstrating the robustness and the efficiency of our method. Text recognition experiments on a database of images extracted from video frames and web pages, with two classical OCRs applied on the obtained bi- nary images show a strong enhancement of the recognition rate by more than 40%.