<p>Rotation estimation is a crucial research field for advancing various domains like autonomous vehicles, aerial warfare, visual navigation, etc. Several digital and optical information processing techniques are used for the rotation estimation. In convolutional pattern recognition, log-polar mapping is an established technique for distortion-invariant pattern recognition. However, there is a scope for information extraction from distortion through combining the benefits of log-polar mapping and optical correlation. The VanderLugt correlator is capable of estimating in-plane rotation by using log-polar mapping. However, the limiting factors are the need for filter synthesis and precise optical alignment. These challenges can be mitigated by using a joint transform correlator architecture. Exploiting this, we propose an in-plane rotation estimation method based on log-polar mapping and a binary joint transform correlator. Correlation signals of a log-polar mapped binary joint transform correlator are observed to suffer displacement along two mutually perpendicular axes when subjected to scale and rotational variation. The rotation information is extracted by finding the relation between rotational variation and shift in locations of the correlation peaks. The concept of the Fresnel lens pattern is applied to maximize the capability, along with other benefits like cost reduction and compactness. Theoretical analysis, simulation study, and optical experiment have been performed to establish the idea of rotation estimation through an optical correlation scheme. The proposed lensless log-polar mapped binary joint transform correlator is found capable of determining full range in-plane rotation with good accuracy. The technique is also immune to scale distortion and can benefit various fields where faster angle estimation is necessary.</p>