Building a Chatbot with Object Detection and OCR

Building a Chatbot with Object Detection and OCR

Over the last several years, chatbot for website are written in Python. They have garnered a great deal of praise and admiration in technology and business. Because these clever bots are so good at mimicking normal human languages and talking with people, businesses in various industries are beginning to embrace them. Everyone, from e-commerce enterprises to healthcare institutions, seems to be using this handy feature to their advantage to achieve commercial benefits.

What are AI Chatbots for Business?

AI Chatbots for website, also known as a chatterbot, bot, artificial agent, etc., is essentially a software program driven by artificial intelligence and aims to carry on a conversation with the user through text or speech. Other names for chatbots include chatterbot, bot, artificial agent, etc. Famous examples are Siri, Alexa, etc.

How Does It Work?

There are many different types of chatbot service providers. They may be distinguished using our categorization scheme, and these two chatbot types are as follows

1.   Rule-Based Approach – A bot is educated using this method, which involves the application of many rules. Using this information, a bot can answer basic inquiries, but it will struggle to answer complicated ones.

2.   Self-Learning Approach – The machine learning method, which is used by these bots and is much more effective overall and is further segmented into two further groups,

  • Retrieval-Based Models – In this method, the bot takes the information provided by the user and selects the most appropriate answer from a list of possible answers.
  • Generative Models – These      models often generate replies rather than looking through a predetermined list of answers, which is one of the characteristics that give intelligent bots their name.

Object Detection Technology

The technique is connected to image processing and computer vision. It is implemented in activities such as face identification and recognition, video object co-segmentation, and other activities of a similar nature. The process involves searching through digital pictures and videos to identify occurrences of certain class items. An input picture may be compared to a particular object model, and each piece that makes up a class has its unique characteristics. For instance, in form-based object identification, the objects are categorized according to their similarities in shape.

How to build OCR Based chatbots & virtual assistant

  1. After you have completed the file, rename it to const.py and place it in the telegram bot directory.
  2. Create a legitimate secret key for settings without key.py by adding SECRET KEY = 'YOUR KEY' to the file and then renaming it     settings.py.
  3. Please make a copy of the static files and place them in the AI directory: TensorFlow graph and breeds.csv should be stored in the root directory of AI, and static header pictures (one per breed, a photo that illustrates it well) should be placed in the root directory of AI's static media.
  4. Create a Docker image with the help of the docker-compose build command, and then run it using the docker-compose-up command.
  5. It is recommended that you forward port 8005 and 8006 to the default port 80 on your server.

What is Google Cloud Vision API?

The Google Cloud Vision API is a pre-trained machine-learning model that may assist in extracting insights from digital pictures. You can get insights such as picture categorization, the identification of faces and landmarks, optical character recognition (OCR), and the tagging of explicit material. Following this link will take you to further information on the Vision API.

Create Dialogflow Agent

  1. Proceed to the Dialogflow Console when you're ready.
  2. Sign in, and if this is your first time using our site, use your address to sign up for our newsletter.
  3. You can access the console after you have accepted the terms and conditions.
  4. Make yourself an Agent.
  5. To create, choose "Create new agent" from the menu when you click on the dropdown arrow in the left-hand window.
  6. You may refer to this as "VisionAPI."
  7. Dialogflow will set up a Google Cloud project for you, allowing you to access logs and cloud functionalities, among other things. You also have the option of selecting an already-completed project.
  8. Once you are ready, click the Create button.
  9. As part of the agent, Dialogflow will construct two default intents for you to use. Default Welcome intent helps welcome your users. The Default Fallback intent might assist in capturing all inquiries your bot cannot grasp.

At this stage, we have a bot that is working, and it can welcome the users. However, we need to tweak it so that users are aware that they may submit a picture to investigate landmarks.

The Applications of OCR by Sector

The OCR systems available today are already quite efficient and can recognize any text readily. Having stated that, their use spans various industries and functions.

Banking sector

Reading in addition to obtaining Checking information, including the ability to identify the account number, the written amount, and the signature. In addition, it is used for processes requiring huge amounts of paperwork.

Insurance Sector

Information may be extracted from papers using OCR in the same way it is in the banking industry. This includes gathering the automobiles involved in the claim, the signatures of the relevant parties, and so on.

Retail Sector

When you go to the grocery store, you won't have to worry about lugging the many coupon stubs that were previously required of you. Most supermarkets have already transitioned to digital receipts, which may be scanned using optical character recognition (OCR) software to get the serial number.

Tourism Sector

Because of technological advancements, the necessity to become fluent in several languages is becoming obsolete in today's world. The same holds for translations that are done automatically.

Conclusion

Today, we have intelligent chatbots & virtual assistant that are driven by artificial intelligence.  There are many Chatbot solution providers. These Chatbot AI business solutions use natural language processing (NLP) to interpret the orders people to give them (through text and speech) and learn from their experiences. The use of AI Chatbot Customer Service has become standard practice for businesses and brands that maintain an active online presence (website and social network platforms)