https://hintonresearchlab.github.io/tag/transformers/TransformersHugo Blox Builder (https://hugoblox.com)enWed, 07 Jan 2026 00:00:00 +0000https://hintonresearchlab.github.io/media/icon_hu8470579392039774497.pnghttps://hintonresearchlab.github.io/tag/transformers/https://hintonresearchlab.github.io/research/unconstrained-compound-characters/Wed, 07 Jan 2026 00:00:00 +0000https://hintonresearchlab.github.io/research/unconstrained-compound-characters/<h2 id="about-this-project">About This Project</h2> <p>This project focuses on the development of intelligent systems for <strong>Unconstrained Compound Character Recognition</strong> using deep learning and computer vision techniques. The primary objective is to accurately recognize handwritten compound characters from unconstrained document images, particularly for Assamese and other Indic scripts.</p> <p>The project addresses challenges such as:</p> <ul> <li>Complex handwritten character structures</li> <li>Variations in writing styles</li> <li>Noise and document degradation</li> <li>Character overlapping and segmentation difficulties</li> <li>Low-resource language datasets</li> </ul> <p>The research integrates:</p> <ul> <li>Deep Learning</li> <li>Optical Character Recognition (OCR)</li> <li>Vision Transformers</li> <li>Sequence Modeling</li> <li>Attention Mechanisms</li> <li>State Space Models (Mamba)</li> </ul> <p>to build robust handwritten script recognition systems.</p> <h2 id="research-objectives">Research Objectives</h2> <p>The major objectives include:</p> <ul> <li>Recognition of unconstrained handwritten compound characters</li> <li>Robust feature extraction from document images</li> <li>Lightweight and efficient OCR architectures</li> <li>Handling complex ligatures and script variations</li> <li>Development of annotated handwritten datasets</li> <li>Improving recognition accuracy under real-world conditions</li> </ul> <h2 id="methodology">Methodology</h2> <p>The proposed framework includes:</p> <ol> <li>Handwritten document image collection</li> <li>Image preprocessing and noise removal</li> <li>Character segmentation and normalization</li> <li>Deep feature extraction using CNN and Transformer architectures</li> <li>Sequence modeling using attention and Mamba-based networks</li> <li>Compound character classification and recognition</li> </ol> <p>The project investigates architectures such as:</p> <ul> <li>CNN-LSTM Hybrid Models</li> <li>Vision Transformers (ViTs)</li> <li>CRNN-based OCR systems</li> <li>Attention-based recognition models</li> <li>Mamba and State Space Models (SSM)</li> </ul> <h2 id="applications">Applications</h2> <p>Potential applications include:</p> <ul> <li>Digital document preservation</li> <li>Historical manuscript digitization</li> <li>Assamese handwritten OCR systems</li> <li>Intelligent archival systems</li> <li>Educational and linguistic tools</li> <li>Regional language computing</li> </ul> <h2 id="results">Results</h2> <p>The project aims to:</p> <ul> <li>Improve recognition accuracy for compound characters</li> <li>Build robust handwritten datasets</li> <li>Develop scalable OCR pipelines</li> <li>Enhance recognition performance under unconstrained settings</li> </ul> <h2 id="future-scope">Future Scope</h2> <p>Future directions include:</p> <ul> <li>Multilingual Indic OCR systems</li> <li>Real-time handwritten recognition</li> <li>Foundation models for document intelligence</li> <li>Large-scale Assamese document digitization</li> <li>Mobile-based handwritten text recognition systems</li> </ul>https://hintonresearchlab.github.io/research/hyperspectral-change-detection-agent/Thu, 07 Aug 2025 00:00:00 +0000https://hintonresearchlab.github.io/research/hyperspectral-change-detection-agent/<h2 id="about-this-project">About This Project</h2> <p>This research project focuses on developing advanced deep learning frameworks for **Hyperspectral Change Detection ** and <strong>Change Captioning</strong> using remote sensing imagery. The work aims to automatically identify significant temporal changes in hyperspectral satellite images and generate semantic textual descriptions of detected changes.</p> <p>The project explores robust AI-driven techniques combining:</p> <ul> <li>Deep Learning</li> <li>Vision Transformers</li> <li>Mamba / State Space Models (SSM)</li> <li>Attention Mechanisms</li> <li>Multimodal Learning</li> <li>Remote Sensing Image Analysis</li> </ul> <p>The primary objective is to improve accurate land-cover change analysis, disaster monitoring, environmental assessment, and intelligent geospatial understanding from hyperspectral imagery.</p> <h2 id="research-focus">Research Focus</h2> <p>The project investigates:</p> <ul> <li>Pixel-level and semantic change detection</li> <li>Temporal feature extraction from hyperspectral data</li> <li>Lightweight and efficient Transformer architectures</li> <li>Mamba-based sequence modeling for long-range dependencies</li> <li>Vision-language models for automated change caption generation</li> <li>Robustness against illumination and seasonal variations</li> </ul> <h2 id="methodology">Methodology</h2> <p>The proposed pipeline includes:</p> <ol> <li>Hyperspectral image preprocessing and normalization</li> <li>Feature extraction using Transformer and Mamba architectures</li> <li>Spatial-spectral attention modeling</li> <li>Change map generation</li> <li>Semantic caption generation using multimodal learning frameworks</li> </ol> <h2 id="applications">Applications</h2> <p>Potential applications include:</p> <ul> <li>Environmental monitoring</li> <li>Forest and vegetation analysis</li> <li>Urban expansion detection</li> <li>Agricultural monitoring</li> <li>Disaster assessment</li> <li>Defense and surveillance systems</li> </ul> <h2 id="results">Results</h2> <p>The project aims to achieve:</p> <ul> <li>Improved change detection accuracy</li> <li>Better semantic understanding of scene changes</li> <li>Lightweight and scalable architectures</li> <li>Enhanced interpretability in remote sensing AI systems</li> </ul> <h2 id="future-scope">Future Scope</h2> <p>Future directions include:</p> <ul> <li>Foundation models for remote sensing</li> <li>Large-scale multimodal geospatial learning</li> <li>Real-time change captioning systems</li> <li>Edge-AI deployment for satellite analytics</li> <li>Self-supervised hyperspectral representation learning</li> </ul> <hr>