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Data Extraction from scanned documents and images
A major problem that many companies are facing today is the easy access to the data available in the documents and images. Almost every process from claim processing to underwritings involves such documents and images in one way or the other. The data inside the documents is important and it takes manually re-keying the data into their management software which eventually becomes a bottleneck for the companies.
For this, there are many software available in the market that helps to automatically extract data from documents and images. But there is another challenge of accurately extracting data. Data in the documents can vary a lot from tables to barcode images which is important as per the use case. Even the format of documents is not consistent across the country.
The technology used in these software is Optical Character Recognition (OCR). OCR is used to automate data extraction processes. OCR has improved over the years but still there are many limitations. One of them is dependency on the templates.
With the use of Artificial Intelligence, we can remove this limitation. AI takes the extraction process well beyond what OCR is capable of.
Let's first understand the limitation of OCR templates or Zonal OCR.
OCR templates limitation
OCR templates is a technology which is used to extract text located at predefined locations. For defining the locations, software first needs to be trained about all the locations of required data fields.
But this method fails, if there is variation in the same document. And in the real world, there are many variations of the same document. For example, identity cards such as PAN card, Aadhar card have many variations.
Also, training the software for each different document and each different variation of the document is a time consuming process. Everytime there is a new variation, you have to train it again. Even, before uploading the documents, you have to manually sort the documents into different variations to prevent any errors in data extraction.
Leveraging the power of AI in OCR based data extraction
With the use of AI, we can find meaning in the data and extract it. There is no need to train the software for the locations of data fields. The two branches of AI - Machine learning and NLP are the solutions for this.
With the use of ML, we can create models that are trained on the large set of data. Larger the data set, more will be the accuracy. With this, data extraction from different variations of a document can be done easily.
Natural language Processing
NLP allows us to understand the context of data. By applying NLP, we can understand the text allowing us to turn raw data into information.
This means that when text is extracted from a document, the AI understands what that text signifies – no need to build new templates and new rules to understand new documents.
AI based Data Extractione
By combining Artificial Intelligence with OCR, companies can remove the bottleneck of manual data entry and improve the operational efficiencies of various processes.
Pibit.ai team has built a Smart Data extracting product using Artificial Intelligence(AI) for the companies to automate their manual data entry processes.