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.
What sets Docsumo apart from the rest in extracting tables from pdf documents?
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.
How can Docsumo impact various Industries
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:
- Banking and Finance Sector:
- Government and BPOs
- Transportation and logistics
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.
Hi, I’m Praneet.
Everyday I speak to people who use our product to automate their workflow. Contact us and we will be happy to see how we can improve your processes.
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A guide to automating data capture from reports, payroll or any other HR-related document into actionable format Accuracy?
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