Abstract: An optical system affixed to an electronic apparatus is provided, including a first optical module, a second optical module, and a third optical module. The first optical module is configured to adjust the moving direction of a first light from a first moving direction to a second moving direction, wherein the first moving direction is not parallel to the second moving direction. The second optical module is configured to receive the first light moving in the second moving direction. The first light reaches the third optical module via the first optical module and the second optical module in sequence. The third optical module includes a first photoelectric converter configured to transform the first light into a first image signal.

Abstract: An optical system affixed to an electronic apparatus is provided, including a first optical module, a second optical module, and a third optical module. The first optical module is configured to adjust the moving direction of a first light from a first moving direction to a second moving direction, wherein the first moving direction is not parallel to the second moving direction. The second optical module is configured to receive the first light moving in the second moving direction. The first light reaches the third optical module via the first optical module and the second optical module in sequence. The third optical module includes a first photoelectric converter configured to transform the first light into a first image signal.

Abstract: An image correction method and an image correction system based on deep learning are provided. The image correction method includes the following steps. An image containing at least one character is received by a deep learning model, and a perspective transformation matrix is generated according to the image. A perspective transformation is performed on the image according to the perspective transformation matrix to obtain a corrected image containing a front view of the at least one character. An optimized corrected image containing the front view of the at least one character is generated according to the image. An optimized perspective transformation matrix corresponding to the image and the optimized corrected image is obtained. A loss value between the optimized perspective transformation matrix and the perspective transformation matrix is calculated. The deep learning model is updated using the loss value.

Abstract: A touch sensing device including a plurality of first sensing electrode sets, a plurality of first conductive lines, and a plurality of sensing sets is provided. The first sensing electrode sets are arranged in an array. The first conductive lines are respectively connected to the first sensing electrode sets. The sensing sets are capacitively coupled to the first sensing electrode sets. Each of the first sensing electrode sets is capacitively coupled to at least two of the sensing sets. One of each of the first sensing electrode sets and each of the sensing sets is a signal transmitter, and the other of each of the first sensing electrode sets and each of the sensing sets is a signal receiver.