Business-consulting

AI/ML Document Data Extraction

OBJECTIVE : To develop a cutting-edge AI and OCR technology-based solution for extracting information from unstructured documents, such as PDFs and images.

HOW WE DID IT : To build a state-of-the-art pipeline for contour detection and OCR technology, we used a DL model for classifying documents based on 10 categories, passed documents through an OCR library for text extraction, filtered the extracted text for desired keywords, and stored the results in a database for further processing.

Our objective was to develop a state-of-the-art pipeline for contour detection and OCR technology to streamline the document processing and information extraction process. Here's how we did it:

We built a pipeline for contour detection to find the edges of documents and crop them separately.
We passed all the documents through a DL model for classifying documents based on 10 categories, such as invoices, receipts, and forms.
After classification, we passed all the individual documents through the latest open-source OCR libraries to convert images to text, such as Tesseract, OCRopus, and GOCR.
We filtered out the text extracted to find the desired keywords from the corpus, such as names, addresses, and amounts.
We stored the results in the database for further processing, such as data analysis, extraction, and integration.

Our pipeline for contour detection and OCR technology is cutting-edge and can efficiently extract information from unstructured documents for enhanced decision-making and streamlined business processes.