Deteksi Teks Secara Otomatis Pada Natural Image Berbasis Superpixel Menggunakan Maximally Stable Extremal Regions dan Stroke Width Transform
Abstract: Text detection in
natural image is something to do before performing character recognition. The
process of text detection plays an important role in the acquisition of
information in an image. This research aims to detect text automatically in
natural image based on superpixels with Maximally Stable Extremal Regions
(MSER) and Stroke Width Transform (SWT). The superpixel method used is Simple
Linear Iterative Clustering (SLIC). The SLIC method is used for segmenting text
images into superpixel spaces. Image segmentation to superpixel aims to group
pixels into homogeneous regions that capture redundant images. SLIC is a
technique that effectively divides images into homogeneous regions
(superpixels). Furthermore MSER is used as a feature to locate the text
candidate region in a segmented image with superpixel. Then edge detection is
done to validate the text area that has been found. Next, the SWT method is
used to distinguish both text and non-text image regions. The dataset used is
ICDAR 2003. Based on test result, MSER with superpixel is able to detect region
of text in natural image. SWT is also able to recover the region which is the
candidate of the text in natural image.
Keywords: Text detection,
Superpixel, Simple Linear Iterative Clustering, Maximally Stable Extremal
Regions, Stroke Width Transform
Penulis: Yohannes
Kode Jurnal: jptinformatikadd170222
