One of the Artificial Intelligence (AI) important applications is human handwritten text recognition. The Handwriting Recognition (HWR) refers to recognizing handwriting through machines. The handwritten text scanned and recognized is offline HWR in nature, whereas recognizing while writing is online HWR Singh et al. [2017]. The handwriting trajectory refers to handwritten strokes which are set of sequential pixels in online HWR and set of pixels in offline HWR Pan et al. [2019]. In offline HWR, these trajectories writing orders recovered through drawing order techniques, and in online HWR, the digital pen strokes refer to trajectories Sharma [2015]. In either case, these trajectories are important sources of information to understand and recognize handwriting. Handwriting trajectories are also understood as the paths traced by a writing pen or stylus movements across a writing surface.

In this paper, we introduce an innovative approach for extracting trajectories from a camera sensor in GPS-denied environments, leveraging visual odometry. The system takes video footage captured by a forward-facing camera mounted on a vehicle as input, with the output being a chain code representing the camera's trajectory. The proposed methodology involves several key steps. Firstly, we employ phase correlation between consecutive frames of the video to extract essential information. Subsequently, we introduce a novel chain code method termed "dynamic chain code," which is based on the x-shift values derived from the phase correlation. The third step involves determining directional changes (forward, left, right) by establishing thresholds and extracting the corresponding chain code. This extracted code is then stored in a buffer for further processing. Notably, our system outperforms traditional methods reliant on spatial features, exhibiting greater speed and robustness in noisy environments. Importantly, our approach operates without external camera calibration information. Moreover, by incorporating visual odometry, our system enhances its accuracy in estimating camera motion, providing a more comprehensive understanding of trajectory dynamics. Finally, the system culminates in the visualization of the normalized camera motion trajectory.