Devanagari Numeral Recognition in Image in Python Projects
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Devanagari Numeral Recognition in Image in Python Projects
Abstract
Optical character recognition (OCR) of Devanagari numerals is important for applications such as automated postal mail sorting, banking, educational grading systems, and historical document digitization. The project Devanagari Numeral Recognition in Image using Python Projects focuses on building an intelligent system that can accurately recognize handwritten or printed Devanagari digits from images. Python is used as the development platform because of its strong libraries for image processing and deep learning, including OpenCV, NumPy, TensorFlow, Keras, and Scikit-learn. The system preprocesses input images to remove noise, normalize size, and enhance contrast, then applies Convolutional Neural Networks (CNN) to extract spatial features and classify numerals. By automating numeral recognition, the system reduces manual effort, improves accuracy, and supports real-time applications in Indian language OCR systems.
Existing System
Existing systems for numeral recognition often focus on Latin or Arabic digits, making them unsuitable for Devanagari script due to its unique shapes and strokes. Traditional methods rely on handcrafted features such as edges, corners, and stroke width combined with classical machine learning classifiers like SVM, k-NN, or Decision Trees. These methods have limited accuracy, particularly for handwritten digits with variations in style, size, and orientation. Additionally, existing systems may not generalize well to noisy or low-resolution images, and require extensive feature engineering. Manual or semi-automated recognition processes are slow and error-prone, which is a significant limitation for large-scale document digitization or real-time recognition tasks.
Proposed System
The proposed system introduces a Python-based deep learning approach for automatic recognition of Devanagari numerals from images. Input images are preprocessed through resizing, grayscale conversion, noise reduction, and normalization to standardize the dataset. A Convolutional Neural Network (CNN) is trained on labeled Devanagari numeral datasets, such as the CMATERdb or ISI Devanagari numeral dataset, to learn spatial features automatically. The CNN includes multiple convolutional and pooling layers to extract hierarchical features, followed by fully connected layers for classification into 0–9 numerals. Data augmentation techniques like rotation, scaling, and translation are applied to improve model generalization. Performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. The proposed system provides a robust, scalable, and automated solution for Devanagari numeral recognition, supporting applications in OCR systems, educational technology, banking automation, and digital document processing.
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