Image-Based Product Classification: ● Designed a MobileNetV2-based CNN with TensorFlow to classify retail products from shelf images. ● Used OpenCV for preprocessing and achieved 92% accuracy using transfer learning. ● Deployed the model via FastAPI, collaborated with cross-functional teams for integration, and supported performance monitoring in internal test environments. Invoice OCR & Field Extraction: ● Built an OCR-based system to extract structured data (Invoice No., Date, Total, Vendor) from semi-structured PDF invoices. ● Used Tesseract OCR with OpenCV preprocessing for improved text clarity. ● Trained a field classifier using TF-IDF and Logistic Regression with 91% extraction accuracy. ● Deployed via FastAPI and integrated with internal finance tools, reducing manual invoice processing by 60%. Product Expiry Date Detection from Packaging: ● Built an OCR-based system using Tesseract and OpenCV to automatically detect and extract expiry dates from printed labels on packaged goods. ● Applied preprocessing techniques such as grayscale conversion, blurring, and adaptive thresholding to enhance text clarity. ● Used regular expressions to validate extracted dates and flag expired products. ● Achieved high reliability in varied packaging conditions. ● Deployed the solution using FastAPI and integrated it with the warehouse dispatch system to prevent the shipment of expired items. Projects: 1. FAQ-Based RAG Chatbot. ● Developed a RAG FAQ Chatbot using LangChain, FAISS and ChromaDB for handling FAQ-based queries. Integrated both vector databases to optimize data retrieval and improve chatbot response accuracy. ● Implemented PDF-based document processing, allowing users to upload files and store embeddings for retrieval. ● Utilized Hugging Face embeddings and a Flan-T5 language model for accurate response generation. ● Built a FastAPI backend with endpoints for PDF uploads and query-based retrieval. ● Applied text chunking techniques using RecursiveCharacterTextSplitter to enhance retrieval accuracy. ● Gained hands-on experience in Vector Databases, LLMs, FAISS, and API development. 2. Intent-Based Chatbot. ● Developed a rule-based chatbot to handle predefined user intents such as greetings, product inquiries, and pricing details. ● Implemented intent-response mapping for structured and efficient query handling. ● Used Gradio to build an interactive user interface for real-time chatbot interactions. ● Applied Python and NLP techniques to improve chatbot response accuracy. ● Gained hands-on experience in chatbot development, user interaction design, and Gradio deployment. 3. Image Generation. ● Built a generative model leveraging Stable Diffusion to create high-quality images from textual descriptions. ● Utilized PyTorch and Hugging Face Diffusers library to implement the architecture and manage training workflows. ● Gained expertise in Generative AI, Stable Diffusion Models, and advanced Python programming for model implementation. 4. Object Detection. ● Designed and implemented an object detection system using the YOLO (You Only Look Once) algorithm to identify and classify objects in real-time. ● Used OpenCV and PyTorch to deploy the model for real-world applications, including live video feeds. ● Gained hands-on experience in Computer Vision, Deep Learning, and efficient object detection frameworks. 5. Image Classification. ● Developed an image classification model using MobileNetV2 as a feature extractor. ● Applied data augmentation techniques (rotation, shifting, zooming, flipping) to enhance model generalization. ● Utilized TensorFlow & Keras to train and fine-tune the model for improved accuracy. ● Processed images using ImageDataGenerator for efficient loading and augmentation. ● Evaluated model performance using test datasets and optimized hyperparameters for better results. ● Gained hands-on experience in Transfer Learning, Deep Learning, and ● Image Processing. 6. Movie Recommendation System. ● Developed a content-based recommendation system using a dataset of Telugu movies. ● Utilized TF-IDF (Term Frequency-Inverse Document Frequency) to extract key features from textual data and calculated cosine similarity to find relevant movies. ● Enhanced user experience by providing accurate and personalized movie recommendations based on user preferences. ● Gained hands-on experience in Python, Pandas, and Scikit-learn, with a focus on NLP techniques. 7. NLP Tasks Using Pretrained. ● Developed a comprehensive NLP system to perform tasks such as Sentiment Analysis, Named Entity Recognition (NER), and Text Generation using pretrained Transformer models from the Hugging Face Transformers library. ● Integrated models like BERT for NER and sentiment analysis, and GPT-2 for text generation, leveraging transfer learning for rapid deployment.