Challenges Faced by Previous Digital Approaches
Before the emergence of AI, banks tried to streamline signature verification using rule-based and image-processing methods. While these techniques showed progress, they encountered difficulties in practical situations.
One of the main obstacles was extracting clear signature images from documents. In reality, signatures often overlapped with text, stamps, or lines, and could be affected by low scan quality, misaligned positioning, or inconsistent placement.
To address this issue, systems relied on inflexible preprocessing pipelines that adjusted brightness, eliminated noise, or isolated specific areas. However, these pipelines were fragile and easily disrupted by variations in document formats.
Furthermore, conventional machine learning algorithms for signature recognition relied on manually crafted features like stroke width, curvature, and slant. These methods lacked flexibility and struggled to adapt to different handwriting styles, resulting in inconsistent accuracy.
