Document information extraction is considered a major challenge in computer vision and involves a combination of object classification and object localization within a scene. The advent of modern advances in deep learning has led to significant advances in object detection, with the majority of research focuses on designing increasingly more complex object detection networks for improved accuracies such as SSD, R-CNN, Mask R-CNN, and other extended variants of these networks. This project is mainly aimed to extract information from invoices using the latest deep learning techniques available for object detection. This deep convolutional neural network model will be introduced for embedded object detection.
Reducing Cost: This helps the organization to cut down the cost of hiring man power for manual data extraction. Employees can be utilized for focusing on other productive job.
Reducing Error: Extracting information from invoices is difficult because of different formats. Also, human errors are another big problem, which leads to data loss and inaccuracy. OCR helps to reduce human error and make the extraction accurate.
Ready Availability: OCR does not required human intervention for the extraction and validation process, once the invoices is fed to the system it will extract the text out of it and push it to the inventory in the same flow.
Security: A complete automated extraction process provides data level security to the organization and the data is not easily visible to outside world.