The manual methodology of document processing is costly, inefficient, and cumbersome to maintain. It is an error-prone process owing to its dependency on human intervention and might be affected due to a lack of visibility and compliance issues.
Extracting data from documents and storing it digitally is a tedious task. A typical employee uses 10,000 sheets of copy paper every year and spends 30-40 percent of their time looking for information locked in email and filing cabinets.
As more customers engage directly with enterprises through the web and mobile in addition to legacy paper and email processes, the real challenge is in gaining total visibility and control over critical data arriving from multiple channels to drive superior business decisions.
A human can look at a document and immediately decipher where invoice numbers are independent of the format of the document. This was, however, not the case with machines before the emergence of Artificial intelligence.
AI has enabled us to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. It has done wonders for data extraction in semi-structured as well as unstructured documents—including manually written forms. Take, for instance, invoice number identification, which usually involves building out complex templates, providing keyword tags and pairings around particular fields and labels, or extracting tables from the documents. We at Docsumo have built our products using this game-changing technology of AI.
In the case of a 500-character page, although an OCR engine might have 99 percent accuracy at the page level, what if the 1 percent erroneous characters are within 5 of the 10 data fields required by the business? Suddenly, this 99 percent accuracy drops to 50 percent accuracy. This is where field-level accuracy comes into play, using what's known as the field-level confidence score.
We have developed algorithms based on Deep Neural Networks and Computer Vision Techniques claiming a field-level accuracy of more than 95 percent for any kind of form. We make use of additional knowledge regarding the language and the context used in a text.
Docsumo is user friendly, and it does not require you to be an expert in the field. It predetermines the field category (date, address, etc) and suggests you the key. It not only allows you to edit the partially correct fields but also helps you to map the fields stored in the database. Docsumo comes with an amazing edit and review tool, which makes it very easy to specify the fields that you want to capture.
Unlike other products in the market for document processing, Docsumo is template independent. It can extract information from unstructured documents as well. You just need to provide a sample of your documents and the platform is smart enough to apply the same to the rest of your documents.
The data in the tables may be present in the invalid format such as invalid date, PAN number, Aadhar number, amount (negative amount), characters and fonts, etc. It provides you suggestions/alerts to correct those fields. It can also be used as prior information for any fraud.
Docsumo helps you to convert the data from various documents into tables which can further be used in analytics to get insights.
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and analyze customer trends and satisfaction, which can lead to new—and better—products and services.
Using AI and Machine Learning, we have developed a system that is intelligent enough to categorize text into more than 80 different labels that include salary, loan, interest, shopping, sell, etc. It provides the user the ability to segregate the data into different fields which can be further used for data analysis.
In the 21st century, due to the advancement of technology, it is relatively easy to commit fraud, and the major part of these frauds belongs to digital transactions. The insurance companies and banks incur huge losses every year due to fraudulent documents. Some of the most common methods implemented by insurers to tackle the menace are by Investigating and cross-checking the documents to detect frauds, perform deep data analytics and statistical analysis.
Docsumo has been a gamechanger for several organizations belonging to numerous sectors by pioneering a basic function - to capture data from any PDF or scanned document. Using intelligent OCR and Artificial Intelligence.
Docsumo decreases the odds of mistakes by 95%. From bank statements to patient records, Docsumo helps in easy extraction of information with high precision in numbers. Alongside this, organizations get an opportunity to work with insights that play an integral role for understanding the current scenario and drafting future plans. There are several parameters for different documents in different sectors.
For example - Banks are more likely to deal with credit card numbers whereas billing will require accurate numbering of transactions made. In order to facilitate this, the data validation function notifies to correct the format and it likewise helps in fraud detections.
We have proudly served the following sectors till date:
To sum up, Docsumo is your go-to tool for table extraction from PDF, independent of any sector you belong to. Automating document workflow by seamlessly integrating Docsumo in your processes helps in sparring a great deal of human effort. Also, it is efficient and effective.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.