4,661 research outputs found
Multi-script text versus non-text classification of regions in scene images
Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4.Comment: This paper has been accepted by IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR 2019
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