answering-genai-exam

Automate exam answer generation by uploading an image of exam questions to extract text and structured information using image recognition, then generate answers using natural language processing and machine learning models.

Yes, the dialogue generation feature creates interactions between the user and AI for each question, providing a step-by-step explanation of the thought process behind the generated answers.

Yes, the tool uses OpenCV and Pillow as dependencies to handle image recognition tasks, extracting text and structured information from image inputs containing exam questions and answer sheets.

Supported output formats include markdown text files, image generation instructions, and source code, allowing you to save the generated answers in various formats for different tasks.

Natural language processing works by using transformers and nltk dependencies to understand the extracted text from images, generating accurate answers and dialogues through machine learning models.

Yes, you need to install transformers and nltk as required dependencies, along with opencv-python and Pillow, to enable the natural language understanding and text generation capabilities.