2 research outputs found
Robust Scene Text Recognition Using Sparse Coding based Features
In this paper, we propose an effective scene text recognition method using
sparse coding based features, called Histograms of Sparse Codes (HSC) features.
For character detection, we use the HSC features instead of using the
Histograms of Oriented Gradients (HOG) features. The HSC features are extracted
by computing sparse codes with dictionaries that are learned from data using
K-SVD, and aggregating per-pixel sparse codes to form local histograms. For
word recognition, we integrate multiple cues including character detection
scores and geometric contexts in an objective function. The final recognition
results are obtained by searching for the words which correspond to the maximum
value of the objective function. The parameters in the objective function are
learned using the Minimum Classification Error (MCE) training method.
Experiments on several challenging datasets demonstrate that the proposed
HSC-based scene text recognition method outperforms HOG-based methods
significantly and outperforms most state-of-the-art methods
Multilingual Scene Character Recognition System using Sparse Auto-Encoder for Efficient Local Features Representation in Bag of Features
The recognition of texts existing in camera-captured images has become an
important issue for a great deal of research during the past few decades. This
give birth to Scene Character Recognition (SCR) which is an important step in
scene text recognition pipeline. In this paper, we extended the Bag of Features
(BoF)-based model using deep learning for representing features for accurate
SCR of different languages. In the features coding step, a deep Sparse
Auto-encoder (SAE)-based strategy was applied to enhance the representative and
discriminative abilities of image features. This deep learning architecture
provides more efficient features representation and therefore a better
recognition accuracy. Our system was evaluated extensively on all the scene
character datasets of five different languages. The experimental results proved
the efficiency of our system for a multilingual SCR.Comment: This paper is under consideration at Pattern Recognition Letter