Confusion network based Video OCR post-processing approach

The paper originally presents a confusion network based framework for Video OCR post-processing. The framework consists of four parts: selection of reference and hypotheses, construction of confusion network, decoding for final output, and a novel metric of quantitatively evaluating Video OCR post-processing approaches. By integrating both visual and textual information, we construct the character transition network to reduce the error rate for OCR outputs. The large-scale experimental results demonstrate that this approach can significantly improve the accuracy of Video OCR results with only little incremental time. Moreover, with comparison and the detailed analysis, we conclude that “Voting+2-gram” is the most applicable method for real application.