Case Study: Deep Learning-Based Data Extraction Solution for Maritime Logistics Company

In today's fast-paced business world, time is of the essence. Companies require a quick and efficient way to fetch custom data from standard documents to streamline their workflow and improve efficiency. This was the case with a maritime logistics data analytics company, which approached us with a request to automate the process of data extraction from standard documents such as Bill of Lading (BLs), Commercial Invoice (CIs), etc.

Problem Statement

The client's existing process for data extraction was manual, time-consuming, and prone to errors. They required a solution that could automate the process of data extraction from standard documents with high accuracy. Our team proposed a deep learning-based solution that could extract data accurately from standard documents, thus improving their overall workflow and efficiency.

Solution Approach

We proposed a solution that utilized deep learning algorithms to extract data accurately from documents with close to 95% accuracy on data extraction and classification. Our team built the solution using the Python programming language and utilized the deep learning framework TensorFlow. We used a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to extract data from documents. The solution was trained using a large dataset of labeled documents to ensure high accuracy.

Implementation

The solution was designed to fetch custom data from several standard documents like Bill of Lading (BLs), Commercial Invoice (CIs), etc. It was integrated within the client's core offering as a SaaS solution, ensuring a smooth transition. The solution worked seamlessly with the client's existing workflow, ensuring that they could access the required information in real-time.

Results

The solution helped the client improve their overall workflow and efficiency by automating the process of data extraction from standard documents. The solution was able to extract data accurately and in a timely manner, resulting in a significant reduction in the time and resources required for data extraction. This led to cost savings and improved efficiency for the client.

Conclusion

The deep learning-based data extraction solution was successful in helping the maritime logistics data analytics company automate the process of data extraction from standard documents. The solution provided accurate and timely data extraction, resulting in improved workflow and efficiency for the client. The success of the solution demonstrates the potential of deep learning in solving complex problems in the logistics industry. Our team is proud to have provided a solution that met the client's requirements and improved their bottom line.