  Many cultures around the world believe that palm reading can be used to
predict the future life of a person. Palmistry uses features of the hand such
as palm lines, hand shape, or fingertip position. However, the research on
palm-line detection is still scarce, many of them applied traditional image
processing techniques. In most real-world scenarios, images usually are not in
well-conditioned, causing these methods to severely under-perform. In this
paper, we propose an algorithm to extract principle palm lines from an image of
a person's hand. Our method applies deep learning networks (DNNs) to improve
performance. Another challenge of this problem is the lack of training data. To
deal with this issue, we handcrafted a dataset from scratch. From this dataset,
we compare the performance of readily available methods with ours. Furthermore,
based on the UNet segmentation neural network architecture and the knowledge of
attention mechanism, we propose a highly efficient architecture to detect
palm-lines. We proposed the Context Fusion Module to capture the most important
context feature, which aims to improve segmentation accuracy. The experimental
results show that it outperforms the other methods with the highest F1 Score
about 99.42% and mIoU is 0.584 for the same dataset.
