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<header class="bg-white dark:bg-gray-800 shadow-sm sticky top-0 z-50">
<div class="container mx-auto px-4 py-3 flex items-center justify-between">
<div class="flex items-center">
<a href="index.html" class="text-2xl font-['Pacifico'] text-primary dark:text-blue-400 mr-8" data-lang-en="AI Text Tools" data-lang-hi="एआई टेक्स्ट उपकरण">AI Text Tools</a>
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<nav id="mainNav" class="hidden md:flex space-x-6">
<a href="index.html" class="nav-link hover:text-primary dark:hover:text-blue-400 text-gray-600 dark:text-gray-300" data-lang-en="Home" data-lang-hi="होम">Home</a>
<!-- Assuming recognizer.html is the main tool page, link from index.html might be better -->
<a href="recognizer.html" class="nav-link hover:text-primary dark:hover:text-blue-400 text-gray-600 dark:text-gray-300" data-lang-en="Recognize" data-lang-hi="पहचानें">Recognize</a>
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<a href="technology.html" class="nav-link text-primary dark:text-blue-400 font-medium" data-lang-en="Technology" data-lang-hi="तकनीक">Technology</a>
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<a href="about.html" class="nav-link hover:text-primary dark:hover:text-blue-400 text-gray-600 dark:text-gray-300" data-lang-en="About" data-lang-hi="बारे में">About</a>
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<span class="text-sm text-gray-600 dark:text-gray-400 hindi-font">हिंदी</span>
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<span class="absolute top-0 right-0 bg-red-500 text-white text-xs w-5 h-5 flex items-center justify-center rounded-full">3</span>
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<div class="px-6 py-4 bg-gray-50 dark:bg-gray-700 border-b border-gray-200 dark:border-gray-600">
<h2 class="text-xl font-semibold text-gray-800 dark:text-white flex items-center">
<i class="ri-flask-line mr-2 text-primary text-2xl"></i> <!-- Flask icon or other relevant icon -->
Technology Stack & Methodology
</h2>
</div>
<div class="p-6 md:p-8 space-y-6 text-gray-700 dark:text-gray-300 leading-relaxed">
<h3 class="text-2xl font-semibold text-gray-900 dark:text-white mb-4">Core Components</h3>
<div class="space-y-4">
<h4 class="text-lg font-semibold text-gray-800 dark:text-white pt-2">1. Deep Learning Model</h4>
<p>
The core recognition engine relies on a deep neural network architecture commonly used for sequence recognition tasks, particularly OCR. This typically involves:
</p>
<ul class="list-disc list-inside space-y-1 pl-4 text-gray-700 dark:text-gray-300">
<li><strong>Convolutional Neural Network (CNN) Layers:</strong> These layers act as feature extractors, automatically learning relevant visual patterns (like strokes, curves, loops) directly from the pixel data of the input word images.</li>
<li><strong>Recurrent Neural Network (RNN) Layers:</strong> Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) layers are often used after the CNN. They process the sequence of features extracted along the width of the image, capturing contextual information and dependencies between different parts of the word. Bidirectional RNNs are commonly employed to consider context from both left-to-right and right-to-left.</li>
<li><strong>Connectionist Temporal Classification (CTC) Loss:</strong> This loss function is essential for training OCR models without requiring pre-segmented character locations. It calculates the probability of the predicted sequence given the input image features, summing over all possible alignments between the input sequence and the target text label.</li>
</ul>
<p class="text-sm text-gray-600 dark:text-gray-400 italic mt-2">
<em>Specific Library Used: TensorFlow 1.15 (as indicated by logs)</em>
</p>
<!-- Optional: Model Diagram Placeholder -->
<!-- <div class="my-4 p-4 border rounded-md bg-gray-50 text-center text-gray-500">Model Architecture Diagram Placeholder</div> -->
</div>
<hr class="my-6 border-gray-200 dark:border-gray-600">
<div class="space-y-4">
<h4 class="text-lg font-semibold text-gray-800 dark:text-white">2. Image Preprocessing</h4>
<p>
Raw input images are processed before being fed into the neural network to ensure consistency and optimal performance. Steps (handled by `SamplePreprocessor.py`) include:
</p>
<ul class="list-disc list-inside space-y-1 pl-4 text-gray-700 dark:text-gray-300">
<li><strong>Grayscale Conversion:</strong> Converting the image to a single channel (grayscale) reduces complexity.</li>
<li><strong>Resizing:</strong> Images are typically resized to a fixed height (e.g., 32 or 64 pixels) while maintaining the aspect ratio. Width may vary or be padded to a maximum length.</li>
<li><strong>Normalization:</strong> Pixel values are scaled to a standard range (e.g., [0, 1] or [-1, 1]) to stabilize training.</li>
<li><em>(Mention any other significant steps like binarization, slant correction if applicable)</em></li>
</ul>
<p class="text-sm text-gray-600 dark:text-gray-400 italic mt-2">
<em>Primary Library Used: OpenCV (`cv2`)</em>
</p>
</div>
<hr class="my-6 border-gray-200 dark:border-gray-600">
<div class="space-y-4">
<h4 class="text-lg font-semibold text-gray-800 dark:text-white">3. Backend Framework</h4>
<p>
The server-side logic, API handling, and coordination are managed by **Flask**, a Python microframework known for its simplicity and flexibility. Flask's responsibilities in this project include:
</p>
<ul class="list-disc list-inside space-y-1 pl-4 text-gray-700 dark:text-gray-300">
<li>Defining URL routes (e.g., <code>/</code>, <code>/recognize</code>, <code>/predict</code>, <code>/api/examples</code>).</li>
<li>Handling HTTP requests (GET for pages, POST for predictions).</li>
<li>Receiving and securely saving uploaded image files.</li>
<li>Calling the Python functions in <code>main.py</code> to trigger image preprocessing and model inference.</li>
<li>Formatting the prediction results into JSON for the API endpoint.</li>
<li>Rendering HTML templates (using the Jinja2 engine) to display web pages.</li>
<li>Serving static assets like CSS, JavaScript, and images.</li>
</ul>
</div>
<hr class="my-6 border-gray-200 dark:border-gray-600">
<div class="space-y-4">
<h4 class="text-lg font-semibold text-gray-800 dark:text-white">4. Frontend Interface</h4>
<p>
The user interacts with the application through a web interface built using:
</p>
<ul class="list-disc list-inside space-y-1 pl-4 text-gray-700 dark:text-gray-300">
<li><strong>HTML5:</strong> Provides the fundamental structure and content of the web pages.</li>
<li><strong>Tailwind CSS:</strong> A utility-first CSS framework used for rapidly building the user interface. It provides low-level utility classes (like <code>p-6</code>, <code>rounded-lg</code>, <code>text-center</code>, <code>font-semibold</code>, <code>hidden</code>) to style elements directly in the HTML markup, promoting consistency and custom design without writing extensive custom CSS.</li>
<li><strong>Vanilla JavaScript:</strong> Handles all client-side interactivity, including:
<ul class="list-['○'] list-inside space-y-1 pl-6 mt-1">
<li>Managing file input selection and example image clicks.</li>
<li>Displaying image previews.</li>
<li>Making asynchronous API calls (<code>fetch</code>) to the Flask backend's <code>/predict</code> endpoint to send the image data.</li>
<li>Receiving and parsing the JSON response from the backend.</li>
<li>Dynamically updating the HTML to show prediction results, loading states, or error messages.</li>
<li>Handling basic UI state changes (e.g., enabling/disabling buttons).</li>
</ul>
</li>
<li><strong>Remixicon / Google Fonts:</strong> Used for icons and specific typography (Inter, Noto Sans Devanagari, Pacifico).</li>
</ul>
</div>
</div>
</div>
</div>
</div>
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<div class="grid grid-cols-1 md:grid-cols-4 gap-8">
<div>
<!-- Add data-lang attributes to footer elements if needed -->
<a href="/" class="text-2xl font-['Pacifico'] text-white mb-4 inline-block" data-lang-en="HindiOCR" data-lang-hi="हिन्दी ओसीआर">HindiOCR</a>
<p class="mb-4 text-sm" data-lang-en="Transforming handwritten Hindi documents into digital text." data-lang-hi="हस्तलिखित हिंदी दस्तावेज़ों को डिजिटल टेक्स्ट में बदलना।">Transforming handwritten Hindi documents into digital text.</p>
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<h3 class="text-lg font-medium text-white mb-4" data-lang-en="Quick Links" data-lang-hi="त्वरित लिंक्स">Quick Links</h3>
<ul class="space-y-2 text-sm">
<li><a href="/" class="hover:text-white" data-lang-en="Home" data-lang-hi="होम">Home</a></li>
<li><a href="/recognize" class="hover:text-white" data-lang-en="Recognize" data-lang-hi="पहचानें">Recognize</a></li>
<li><a href="/examples" class="hover:text-white" data-lang-en="Examples" data-lang-hi="उदाहरण">Examples</a></li>
<li><a href="/technology" class="hover:text-white" data-lang-en="Technology" data-lang-hi="तकनीक">Technology</a></li>
<li><a href="/about" class="hover:text-white" data-lang-en="About" data-lang-hi="बारे में">About</a></li>
</ul>
</div>
<div>
<h3 class="text-lg font-medium text-white mb-4" data-lang-en="Resources" data-lang-hi="संसाधन">Resources</h3>
<ul class="space-y-2 text-sm">
<li><a href="#" class="hover:text-white" data-lang-en="Documentation (TBD)" data-lang-hi="प्रलेखन (TBD)">Documentation (TBD)</a></li>
<li><a href="#" class="hover:text-white" data-lang-en="GitHub Repo (Link)" data-lang-hi="गिटहब रेपो (लिंक)">GitHub Repo (Link)</a></li>
<li><a href="#" class="hover:text-white" data-lang-en="Support (TBD)" data-lang-hi="समर्थन (TBD)">Support (TBD)</a></li>
</ul>
</div>
<div>
<h3 class="text-lg font-medium text-white mb-4" data-lang-en="Contact (Project)" data-lang-hi="संपर्क (परियोजना)">Contact (Project)</h3>
<ul class="space-y-2 text-sm">
<li class="flex items-start">
<i class="ri-mail-line mt-1 mr-2"></i>
<span>khansamayashaswini@gmail.com</span>
</li>
<li class="flex items-center">
<i class="ri-building-line mr-2"></i>
<span>MATS University,Raipur / NIC Durg</span>
</li>
</ul>
</div>
</div>
<!-- Copyright -->
<div class="border-t border-gray-700 mt-8 pt-8 text-center">
<p class="text-sm">© 2025 Yashaswini khansama | Final Year Project | NIC</p>
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