Document automation using OCR and Deep Neural Networks

Manually extracting the fields of a form can be a tedious and time-consuming task, especially when dealing with large volumes of documents. To automate this process, we have developed a procedure to extract the fields of a form using Optical Character Recognition (OCR) and Neural Networks 

Our aim is to provide a tool that can accurately and efficiently extract the fields of a form from an image or scanned document. By using OCR andNN, our tool is able to recognize and classify the text and elements in the form and extract the desired fields with high accuracy.

To implement the tool, we made the following assumptions:

1. The form images or scanned documents are of high quality and contain clear and legible text.
2. OCR and NN can effectively recognize and classify the text and elements in the form and extract the desired fields.

To develop the tool, we followed the following steps:

1. Data preparation: We prepared the form images or scanned documents for OCR and DANN by performing preprocessing tasks such as de-skewing and binarization to improve the quality and legibility of the text.
2. OCR: We used OCR to recognize and extract the text from the form images or scanned documents.
3. NN: We used NN to classify the text and elements in the form and extract the desired fields. We trained the ANN model using a labeled dataset of form images or scanned documents, where the desired fields were annotated by hand.
4. Validation: We validated the accuracy of the extracted fields by comparing them to the annotated fields in the labeled dataset.

We hope that our tool will provide a useful and convenient way to extract the fields of a form from images or scanned documents. With the use of OCR and NN, our tool is able to accurately and efficiently recognize and classify the text and elements in the form and extract the desired fields.