Abstract

Signature forgery is causing major dysfunctions across public and private industries as it results in the impersonation of individuals with significant limitations. With the right image processing tools in action, one can detect and recognize characters and patterns to mark off the false signatures. This layered security approach in examinations to validate students has a beneficial impact in streamlining the whole authentication process.

Problem

Impersonation in examinations is a major issue that provokes numerous cases with illegitimate benefits. False signatures can greatly affect the examination method by helping undeserving candidates, where the right individuals are denied. This potential forgery makes it tough for the board of directors to manually validate each candidate.

Solution

Timing Technologies has implied Signature Detection technology with AI to solve the issue. Signature verification is executed on candidate attendance data. With deep learning algorithms, we can determine even the minor differences from the original copy. Applied Pytesseract to recognize characters in the signature and match it with the registered signature.

The algorithms in play are capable of identifying multiple false signatures at once which simplifies verification. We load the trained signature model into the system and make use of the PyTesseract tool for detecting number configuration from an image. The signature from the cropped image is further cropped and saved individually by the reg no. of each candidate.

With Convolutional Neural Networks (CNN) and the signature data, we train the signature classification model. This process has an accuracy rate of 90% and has a significant potential for upgrading attendance checks in examinations.

Benefits

Automation: With the AI-powered approach, the signature verification process eliminates hands-on work and helps to accomplish our authentication goals easier.

Time-saving: Through this approach, we can quickly handle large-scale data while being cost-effective. This simultaneously increases the efficiency of the process.

Flexible: Real-time signature verification is performed with fewer process delays and high-performance speed.

Transparent: Ensuring great transparency and greater productivity, this software solution retrains the model, enhances accuracy, and drives.

Conclusion

Our signature-based detection with deep learning and CNN models provides a streamlined solution to verify attendance in examinations. Our software solution uses advanced algorithms to detect and recognize multiple signature databases, which accelerates the verification process and increases accuracy over time. With our solution, you can be confident that attendance is being verified accurately and efficiently, providing a smoother experience for both exam administrators and attendees.