A image processing and deep learning algorithm

I am looking for a skilled freelancer who can develop an image processing and deep learning algorithm for a specific task. The task involves template matching for medical claims documents. Skills and Experience: - Strong knowledge and experience in image processing and deep learning algorithms - Expertise in object recognition, image segmentation, and image classification - Ability to generate synthetic data for training the algorithm - Familiarity with medical claims documents and their template structures - Proficiency in achieving moderate accuracy levels (70-90%) for the algorithm. project details: Similar Document Template Matching Algorithm for medical insurance claim A challenge when dealing with a large volume of medical invoices, prescriptions, and lab test reports received for medical insurance claim from numerous providers, including doctors, hospitals, and labs, as well as from customers. Need to develop similar document templates algorithm. The primary issue we face stems from fraudulent customers who exploit identical digital or printed templates, with minor modifications such as changing the provider names, logos, colors, and text content positioning. By manipulating the customer details within these templates, they attempt to file reimbursement claims that are difficult to detect using standard document comparison checks. To tackle this problem, we require the development of a robust Similar Document Template Matching Algorithm. This algorithm will automate the process of template extraction and standardization, allowing us to identify similarities and patterns across various documents. By comparing the templates used in different claims, we can effectively identify instances of potential fraud, even when the textual content has been altered. The algorithm should be designed to handle a high volume of documents from diverse providers and customers. It should be capable of identifying commonalities in template structure, design elements, and formatting, while also accounting for variations resulting from legitimate differences between providers Key objectives of the Similar Document Template Matching Algorithm: 1. Template Extraction 2. Template Comparison 3. Fraud Detection 4. Flexibility: Design the algorithm to accommodate variations in template design and content across different providers, while maintaining the ability to detect fraudulent patterns The dataset should encompass different document types, such as medical invoices, prescriptions, and lab test reports, reflecting the real-world scenarios the algorithm will encounter during deployment. By using this self-generated dataset, the algorithm can be trained and evaluated under realistic conditions. This approach enables the algorithm to learn patterns and similarities that exist in actual documents, allowing it to accurately detect fraudulent claims and identify suspicious patterns across different providers and customers.