Adapting off-the-shelf CNNs for word spotting & recognition

Abstract

The word spotting approach is extremely useful for searching and annotating documents for which robust recognizers are unavailable. Traditionally, hand-designed features were used to represent the word images for spotting. In this paper, we learn a data-driven representation for word-images from Convolutional Neural Networks (CNNs). Previous approaches that learn deep neural networks for a particular task/dataset are difficult to design and train for generic word spotting. Instead, by “adapting” a CNN trained for a different problem, we show tremendous speedup in the training phase. Our experiments show that features extracted from an adapted-CNN handsomely outperform hand-designed features on both spotting and recognition tasks for printed (English and Telugu) and handwritten (IAM) document collections.