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Word Recognition with Deep Conditional Random Fields
Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text
overlap with arXiv:1412.339
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