The Power of AI in Touchless Automation: Streamlining Processes

Photo by Alex Knight on Unsplash

Touchless automation using advanced AI refers to the use of artificial intelligence (AI) and machine learning (ML) techniques to automate tasks without human intervention. It is often used to automate repetitive, time-consuming, and error-prone tasks, such as data entry, document processing, and customer service.

There are several types of advanced AI techniques that can be used for touchless automation, including:

1. Computer Vision: Using AI-powered image recognition and object detection to automatically process visual data such as images, videos, and documents.
2. Natural Language Processing (NLP): Using AI-powered natural language understanding and generation to automatically process and understand text data such as emails, chat messages, and voice transcripts.
3. Robotics: Using AI-powered robots to automate physical tasks such as sorting, packaging, and assembly.
4. Predictive modeling: Using machine learning techniques to predict future outcomes and make decisions without human intervention.
5. Reinforcement learning: a type of machine learning where the agent learns to make decision based on trial and error and feedback from the environment.
6. Generative models: using deep learning techniques such as GANs (Generative Adversarial Networks) to automatically generate new data.

Touchless automation using advanced AI can provide significant benefits to businesses, including increased efficiency, reduced costs, and improved accuracy and consistency of tasks. However, it does require significant investment in terms of data, technology and expertise to build and deploy such systems.

Today, we will be focusing on the first problem set, where we were able to solve the problems in billing for a one of the biggest retail chains using Computer Vision, Ui Path.

Problem: Text recognition and Text extraction from a bank cheque Image.

Architecture: The architecture of the model as follows

Generally, we write a code for text recognition and text extraction, and we will run that file whenever it is required. In this process we need to run the file and update the images manually. To avoid human intervention here we integrate our code with Ui-path (process automation tool).

Using Ui-path, we need not run and update the images manually, we need to create a process for updating the images and running our code file. Once the process is created, we just need to run the bot and it will take care of updating the images and it will also trigger source code file to execute and to produce desired output.

Solution: Using pytesseract and Open CV we can extract the text from an image.

One simple process is to create a boundary box around the required information area and apply pytesseract on top of that, it will extract the text from the image. however, this pytesseract is limited to printed text.

Next steps: We are creating a model for handwritten text recognition and extraction along with zone of interest identification which will idenitfy selective segments from invoices, product labels, bank cheques, and more.

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