HandWrittenImageRetrieval is a project focused on developing a system for efficiently retrieving handwritten images from a large dataset. This system aims to simplify the process of finding images with similar handwriting styles, which is a challenging task due to the diverse and complex nature of human handwriting.
In large collections of handwritten documents, such as historical archives or personal collections, finding a specific document or documents with similar handwriting can be like searching for a needle in a haystack. Manual search is time-consuming and inefficient. HandwriteSeek aims to automate this process by using image processing and machine learning techniques to identify and retrieve handwritten images based on their similarity to a given query image.
Our initial approach to tackling this problem involves several key steps:
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Data Preparation: Standardize and preprocess the handwritten images to a uniform format. This includes converting images to grayscale, resizing, and perhaps binarizing them for simplicity.
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Feature Extraction: Extract distinctive features from each image that capture unique aspects of the handwriting. Initially, we'll use simple features like Projection Profiles, which provide a basic representation of the handwriting style.
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Similarity Measurement: Develop a method to measure the similarity between handwritten images. Initially, we will use straightforward techniques like Euclidean distance, with plans to explore more sophisticated methods like Cosine Similarity or Dynamic Time Warping in the future.
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Retrieval Mechanism: Implement a basic retrieval system, possibly starting with a simple K-Nearest Neighbors (KNN) algorithm, to find and return images most similar to a query image.
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Evaluation: Assess the system's performance using metrics such as Mean Average Precision (MAP) to ensure effectiveness and accuracy.
As the project evolves, we plan to incorporate more advanced machine learning models and more robust feature extraction techniques. We also aim to develop a user-friendly interface for easy interaction with the system.
We welcome contributions from the community.
For any queries or suggestions, feel free to contact me at [mayankbaluni@gmail.com]