The graph neural networks has been proved to be an efficient machine learning
technique in real life applications. The handwritten recognition is one of the
useful area in real life use where both offline and online handwriting
recognition are required. The chain code as feature extraction technique has
shown significant results in literature and we have been able to use chain
codes with graph neural networks. To the best of our knowledge, this work
presents first time a novel combination of handwritten trajectories features as
chain codes and graph neural networks together. The handwritten trajectories
for offline handwritten text has been evaluated using recovery of drawing
order, whereas online handwritten trajectories are directly used with chain
codes. Our results prove that present combination surpass previous results and
minimize error rate in few epochs only.

该研究论文通过将手写轨迹特征与图神经网络相结合，评估了离线手写文本的恢复绘图顺序以及直接使用链码的在线手写轨迹，实证结果证明这种组合超过了以往的结果，并且在短时间内最小化了错误率。

基于图神经网络的手写轨迹识别

Graph Neural Network based Handwritten Trajectories Recognition

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.

通过视觉里程计从 GPS 受限环境中的相机传感器中提取轨迹的创新方法，系统利用车辆上安装的前置摄像头捕捉的视频素材作为输入，输出为表示摄像头轨迹的链码。该方法基于相位相关对视频连续帧进行信息提取，并引入基于相位相关的 x 轴偏移值的新型链码方法即 “动态链码”，通过建立阈值来确定方向变化（前进、左转、右转），并提取相应的链码。该系统不依赖于空间特征的传统方法，在嘈杂环境中表现出更高的速度和稳健性，而且不需要外部摄像头校准信息。通过融合视觉里程计，该系统提高了对相机运动的精确估计的准确性，提供了对轨迹动态的更全面的理解。最后，系统将归一化摄像头运动轨迹可视化。