How Can I detect Signature on a PDF and Image?

In today's digital age, most business transactions are conducted electronically. However, some occasions still require a physical signature, which can be challenging for businesses that aim to minimize paper-based processes.

This is where Signature Detection comes into play. Signature Detection is a computer vision solution that uses algorithms to identify and authenticate signatures on various document types, including PDF and image files. Automating the signature verification process saves time and reduces the risk of errors, making it an essential tool for businesses.

One of the primary benefits of Signature Detection is its ability to improve efficiency. By automatically detecting and verifying signatures, businesses can streamline their document management processes, reducing the time and effort required to manually review and authenticate signatures. This is especially valuable in industries such as banking, insurance, and legal services, where document processing is a crucial part of day-to-day operations.

In conclusion, Signature Detection is a technological solution that can help businesses optimize their workflow by reducing the time and effort needed to authenticate signatures. It is a valuable tool that improves accuracy and reduces the risk of fraud, making it an essential tool for businesses in the digital age.

Today we launched a new model to detect signature on a visual document. You can access the model in here.

import cameralyze

# connect to Cameralyze
connector = cameralyze.Model(api_key="YOUR_API_KEY")

# set the model you want to use
connector.set_model(model="575b9607-71d0-466f-bc1c-b3b6064bce22")

# run for image URL
image = "https://"

# run for local file
image = model.read_file(path="")

# run for base64 file
import base64
with open("yourfile.jpg", "rb") as image_file: 
  image = base64.b64encode(image_file.read()).decode("utf8")
  
  
# get response
response = model.predict(image=image)