Products I worked so far..
IDP (INTELLIGENT DOCUMENT PROCESSING )
Successfully led the development of a comprehensive end-to-end intelligent document processing pipeline that harnesses the power of advanced AI and machine learning technologies. By seamlessly integrating Large Language Models LLaMa 3.1 / Gemma2 (LLMs)with cutting-edge OCR technologies, this pipeline revolutionizes the extraction of data from complex documents, transforming unstructured information into actionable insights with exceptional accuracy and speed.
Key Highlights:
Optimized Inference Performance:
Leveraged ONNX Runtime to significantly improve the inference speed and efficiency of deployed models, ensuring faster processing times for large volumes of documents.
Advanced PDF Extraction:
Implemented specialized PDF extraction utilities to accurately extract text, images, and metadata from various PDF formats, enhancing the quality and completeness of data retrieval.
Table Detection and Extraction:
Developed a table detection model using the YOLO architecture to precisely identify and extract tabular data from documents, enabling structured data analysis even in challenging document formats.
Image Enhancement with GANs:
Utilized Generative Adversarial Networks (GANs) for image enhancement, effectively reducing noise and improving the clarity of document images, which in turn boosts OCR accuracy and data quality.
Intelligent Document Indexing:
Employed a BERT model for content-based document classification, providing high-relevance indexing and categorization that ensures easy retrieval and management of documents.
Image-Based Document Classification:
Developed and deployed models using TensorFlow and Keras to accurately classify image-based documents, streamlining document organization and searchability.
Programming Languages used :
C / C++ / Python
This multi-faceted approach combines state-of-the-art deep learning models and AI techniques, creating a powerful and efficient solution that addresses the challenges of complex document processing and delivers exceptional results.
Ischemic Stroke Diagnosis - AI-based MRI Image Analysis (BRANE ENTERPRISES)
Problem Statement:
Developed an advanced AI-driven application to assist medical professionals in diagnosing ischemic strokes using MRI scans. The project aimed to improve the detection and treatment timeline within the critical 4.5-hour "Golden Period," using cutting-edge machine learning techniques to identify ischemic strokes from MRI images.
Objective:
Analyzed MRI scans from leading hospitals to uncover patterns and correlations that can enhance diagnostic
accuracy, ultimately improving patient outcomes.
Methodology and Key Contributions:
Data Preprocessing:
Collected anonymized MRI images from hospitals while ensuring compliance with data protection regulations.
Converted DICOM images to PNG format for model training and removed noisy data for enhanced image clarity.
Metadata Handling and Image Classification:
Extracted metadata to bucket MRI sequences (DWI, ADC, FLAIR, SWI, MRA) based on stroke protocol.
Classified images without metadata using machine learning models like SVM, CNN, and VGG16.
Image Labeling and Segmentation:
Labeled infarcts and artifacts in MRI sequences for precise training of the segmentation model.
Utilized UNET for image segmentation, identifying Regions of Interest (ROI) to isolate infarcts from artifacts.
Machine Learning and Model Validation:
Fine-tuned the UNET segmentation model for high-accuracy infarct identification.
Implemented a novel algorithm to detect DWI and FLAIR mismatches, crucial for determining if the stroke occurred within the 4.5-hour golden period.
Infarct Detection Logic:
Developed an algorithm comparing DWI, ADC, and FLAIR images to identify infarcts and estimate infarct timing, critical for determining treatment eligibility.
Model Deployment and Real-Time Assistance:
The AI model integrates with a mobile application for remote radiologist validation.
The application aids doctors in making informed decisions on thrombolysis treatment based on infarct timing.
Technologies Used:
Python, Machine Learning (SVM, CNN, VGG16), Image Processing, DICOM, PNG, Metadata Handling, UNET Model, MRI Image Segmentation, Radiologist Decision Support System.
CMR+ ( Preprocessing Module ) (ANTWORKS)
Comprehensive preprocessing module for scanned images that significantly enhances document quality and readability, leveraging a combination of powerful libraries and techniques. This module incorporates OpenCV and Leptonica for advanced image enhancement, providing capabilities such as denoising to remove noise and artifacts, and image enhancement using histogram-based techniques to improve contrast and clarity. Additionally, the module employs Tesseract for orientation correction, ensuring that rotated or skewed documents are automatically aligned for optimal processing.
Key functionalities include text inversion to handle documents where text appears in a negative format, enabling accurate text recognition and extraction. The module also features robust watermark removal techniques that clean up scanned documents by eliminating unwanted watermarks or background patterns, further improving the quality of the extracted information. This preprocessing pipeline ensures that documents are prepared in their best possible form for subsequent processing stages, enhancing the accuracy and efficiency of OCR and other downstream tasks.
Key Highlights:
Denoising: Utilizes advanced filters to remove noise and artifacts from scanned images, enhancing overall document clarity.
Orientation Correction: Automatically detects and corrects rotated or skewed documents using Tesseract, improving alignment for accurate text extraction.
Text Inversion Handling: Efficiently processes documents with inverted text to ensure proper OCR functionality.
Image Enhancement: Applies histogram-based techniques for contrast adjustment, enhancing the readability of text and other important features.
Watermark Removal: Implements specialized algorithms to detect and remove watermarks or unwanted background patterns, resulting in cleaner and more professional-looking documents.
Programming languages used :
Python/C/C++
This preprocessing module is a critical component in intelligent document processing pipelines, ensuring high-quality input for reliable data extraction and information retrieval.
Satellite Imaging ( CROP / SHRIMP FORMING ANALYSIS )
I developed a sophisticated crop analysis and shrimp farming monitoring solution using Sentinel-2 satellite imagery, which leverages advanced remote sensing techniques and machine learning algorithms to predict crop conditions and optimize aquaculture management. This solution focuses on extracting key features such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and other spectral indices that are crucial for assessing vegetation health, water content, and overall environmental conditions.
By processing multi-spectral satellite images, the solution calculates NDVI to monitor vegetation vigor and crop health, providing insights into areas that require intervention. NDWI is utilized to assess water content and moisture levels, critical for both crop irrigation management and shrimp pond monitoring. Other indices, such as the Soil-Adjusted Vegetation Index (SAVI) and Modified Soil-Adjusted Vegetation Index (MSAVI), are also extracted to provide a comprehensive understanding of soil conditions and vegetation cover, reducing the effects of soil background on vegetation analysis.
Technical Highlights:
Feature Extraction from Satellite Imagery:
Utilized Sentinel-2 satellite data to extract multiple spectral indices (NDVI, NDWI, SAVI, MSAVI) to analyze vegetation and water conditions accurately.
Machine Learning for Predictive Modeling:
Applied a range of machine learning algorithms, such as Random Forests to predict crop conditions and aquaculture suitability based on extracted features.
Time-Series Analysis for Temporal Insights:
Implemented time-series analysis to monitor changes over time, enabling the detection of trends and anomalies in crop health and water quality, and allowing for proactive management.
Data Preprocessing and Normalization:
Developed a robust data preprocessing pipeline to handle noise and variability in satellite images, including radiometric correction, atmospheric correction, and cloud masking, ensuring accurate feature extraction and reliable model predictions.
Geospatial Data Integration:
Integrated GIS (Geographic Information System) tools to map and visualize crop and aquaculture areas, providing intuitive spatial insights and aiding decision-making processes.
Automated Alerts and Recommendations:
Designed a system to generate automated alerts and actionable recommendations based on model outputs, enabling farmers and aquaculture managers to make timely and informed decisions to improve yield and sustainability.
This solution combines the power of remote sensing, geospatial analysis, and machine learning to provide a comprehensive tool for monitoring and managing crop health and shrimp farming, leading to optimized resource utilization, early detection of potential issues, and enhanced productivity in agriculture and aquaculture sectors.