Semantic and Verbatim Word Spotting using Deep Neural Networks
2016 (English)Conference paper (Refereed)
In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform wordspotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.
Place, publisher, year, edition, pages
handwritten word spotting, convolutional neural networks, deep learning, word embeddings
Computer Vision and Robotics (Autonomous Systems)
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-306667OAI: oai:DiVA.org:uu-306667DiVA: diva2:1044046
International Conference on Frontiers in Handwriting Recognition (ICFHR), October 23-26, 2016, Shenzhen, China.
FunderSwedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1