Many multinational companies today, face the most significant revolutionary change in the realm of technology. Namely, conversion of paper documents to digital and the inability to leverage data ensnared inside scanned documents and images. Relying on data trapped inside documents and manually re-keying them can land businesses into a bottleneck situation. Therefore, the need for an automated solution to segment and extract document elements is high.
One such need stands for the retrieval of data from stamp instances. Despite the enormous utilization of computer technology in various areas of our lives, paper documents still play an essential role. Contracts, certificates, invoices, wills, and all documents issued by formal authorities are printed on the paper, and a signature or a stamp guarantees its authenticity.
Initially, manual data extraction of stamps led to errors and excessive time consumption. As technology improved, businesses used digital systems to cut down on errors and reduce time frames using such platforms as OCR; the only solution for scanning stamps and capturing data at the time.
Despite significant progress in OCR technology, the problem of stamp automatic recognition remains open. The difﬁculty of stamp extraction is that there is no standard template. It is a partially graphical and textual object which can be placed at any position in the document. The variations are in its shape and color, print quality or rotation, and even imprints (noise, inconsistency, gaps, stains, etc.). Many entrepreneurs fail to understand this approach cannot detect and classify such diversity in stamps.
The challenge with OCR technology is it is responsible for text extraction only from the stamps or any image; it neither extracts the stamps nor classifies them. This is particularly challenging as stamps present in the scanned documents often contain:
- Random orientation
- Overlapping background text
- Words are written around the circular boundary
Businesses are starting to turn to AI-driven alternatives to boost their efficiency and extract meaning. Over the past few years, deep learning, a subtype of machine learning, and the modern wonder of artificial intelligence have enabled rapid progress even surpassing human-level performance.
Infrrd’s IDC platform is a forward-looking approach with a blend of cognitive capabilities. It covers several interlinked AI-enabled capabilities, such as deep-learning-enabled object detection, pattern recognition, and image recognition.
The process involves the ingestion of a substantial amount of unstructured documents in images or scanned documents, followed by stamp identification from those documents, and extraction of crucial attributes from those stamps (i.e., date, country, company name, logo, etc.). In tandem with the process, our platform also detects cognitive patterns that mimic human judgment to help attain higher levels of precision than the average human reviewer.
Infrrd’s IDC stamp detection process
Infrrd’s IDC platform can effectively extract attributes from stamps in a shorter timeframe with better accuracy. It also holds the capability to extract data from stamps that are overlapped with signature or text. The module also optimizes the stamps by reorienting them to get accurate data. With all the AI capabilities in place, the decision-making process is made easier with better business outcomes without compromising on quality.
Download our free case study to get a deeper understanding of how Infrrd’s AI-enabled platform extracted interactive data from stamp images inside various documents.
Connect with us today for a consultation or a free demo to understand more about Intelligent Data Capture and it is abilities.