Accurate data extraction from invoices, purchase orders, receipts, and other such documents is the need of the hour. Manual data entry is a highly time-consuming process and adds costs in terms of human resources requirements. High error rates are another issue with processing documents manually. And in case there is a peak business season, the backlogs of processing the documents and ensuring every supplier or vendor get payment on time, becomes a hectic task.
Traditional OCR systems have been placed for years which enables surface-level data extraction. But with the help of machine learning models and smart OCR technology, KlearStack AI contextual understands the document and then proceeds to extract data from the document. Apart from that, as more and more documents are processed through KlearStack AI, the platform self-learns and evolve to increase the data extraction accuracy. This is possible thanks to the machine learning models put in place.
KlearStack AI follows the ETL process of data extraction: Extraction, Transformation & Loading.
This is the very first step in the process of data extraction. Raw data from various sources is copied here. Relevant data is extracted from that raw data. Emails, PDFs, scanned images other such types of documents are data sources from which KlearStack AI can extract data.
Once the data has been successfully extracted from the documents, it is now organized and made uniform. Say there are two invoices which have two different column headers with the same meaning as “Item Description” and “Description”. At this stage, KlearStack will ensure that all the column headers and other such data have the same column header saying “Description”. This is one of the many examples of data transformation which takes place at this stage.
This is the last stage of data extraction. Once the data is transformed, it is stored in one single location. This could be a cloud-based data warehouse or a data storage location that is being used by your enterprise. This becomes the single source to find all the information that has been extracted and transformed seamlessly.
Examples of Line Item Data Extraction
Figure 1: Multiple line item data extractions
KlearStack AI has the ability to extract multiple line item data accurately. In Figure 1, there are about ten-line items and four columns. All this data is extracted with 100% accuracy as you can see on the right side of the Figure 1 image.
Apart from accurate data extraction, it also transforms and uniforms the data. As you can see on the left side of Figure 1, there is a column header with the title “Price”. KlearStack AI understood that this column has prices or rates for each description item and translates “Price” to “Unit Rate”. This standardizes the data from documents and data is transformed accurately.
Figure 2: Merged column data extraction
Another example where the data from each line item is extracted accurately. In Figure 2, notice the columns “Units” on the left side and on the right side. The KlearStack AI understood that these are merged columns based on the way the table has been designed in this particular invoice.
This is the reason why KlearStack AI did not extract data for each line item separately for the columns “Ordered & Shipped” under the columns “Unit”. However, you can still notice a gap on the right side between the two numbers “92 92”. This gap indicates that these are two separate columns from a single merged column header.
Technologies Used in KlearStack AI
We have seen and understood how KlearStack AI extracts data from each line item with two different examples. Now it is important to understand what are the technologies that are being used by KlearStack AI, that makes this platform so unique and different from the rest.
Just like how our eyes operate, computer vision operates in a similar fashion. Computer vision has the ability to scan the entire document, slice it into pieces and understand each text or image on a document individually and contextually. This allows KlearStack AI to understand what data is placed where on a document. Accurate data extraction for line items is not possible without a thorough scanning of the document.
Natural Language Processing (NLP) understands the text on a document as the person has intended to. The objective here is to understand the intent of the words rather than just extracting the word as it is. This helps in the data transformation stage wherein the text has to become more standardized.
KlearStack AI has self-evolving machine learning technology in place that enables adaptive learning. The more documents are processed, the better platform becomes and achieves higher accuracy. KlearStack learns from the manual inputs that are being fed in case of any errors and therefore, can enable you to achieve higher accuracy with almost zero human intervention.
The KlearStack Advantage
KlearStack AI achieves higher accuracy thanks to the technologies upon which the platform is built. Its uniqueness in terms of the way it can extract data accurately for each line item while retaining the content in its intended form is what makes this platform stand out from the rest. Watch the video here to know how KlearStack AI works.