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    What Is Invoice Data Capture?


    Processing a vendor bill from the receipts by capturing the invoiced data via scanning methods such as optical character recognition (OCR) is called invoice data capture. 

    The traditional processes involved with capturing billing information via manual input of data is as follows:

    1. Receive the paper bill
    2. Log in to the invoicing app
    3. Crosscheck purchase order with the paper bill by entering the PO number in the software
    4. Crosscheck the vendor detail with the paper bill by entering the name of the supplier into the software

    You have to continue by repeating the crosscheck process mentioned above manually for every field to complete it. 

    Unlike this tedious process, the data capture process via OCR typically consists of four steps that include the process of extraction with OCR, classification of the document, validation with quality control of data, assessment, and analysis.

    Read on to discover the meaning of OCR along and its two types along with the scanning systems worth using for structured and semi-structured documents.

    What Is OCR?

    Built for enhancing readability for blind readers in the early 20th century, optical character recognition, commonly referred to as OCR, is used to read license plates, receipts, invoices, checks, and legal billing documents. 

    The process involves scanning a document to read and collect its content using software or hardware. Accounting professionals make use of OCR to analyze billing details by transforming them into metadata that fills databases. 

    Simply put, such a tool can collect data from invoices automatically without a manual look. It transforms the AP workflow and when combined with machine learning, such a system can intuitively fill the appropriate fields from the extracted information. 

    There are two types of optical character readers as explained below.

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    Template-Based OCR 

    This system demands manual maintenance after the optical reader collects information based on pre-built templates and algorithms. Physical personnel must ensure that the optical reader collects and fills the collected data in the correct fields regardless of differing invoicing templates and layouts. 

    Template-based optical readers use unique rules to process the varied billing templates they receive. This accurate way of capturing the billing data is easy if your vendors can comply with a single template requirement too. 

    For example, if you have 15 clients and all of them use 15 different formats for invoicing, you will face a hard time defining rules for each template. Instead, if you were to provide or request that your clients use one template for invoices, you don’t make more than one rule. However, keep in mind that this type of OCR requires at least one employee to maintain accuracy. 

    Smart OCR Invoice Scanning   

    Referred to as the cognitive type of data capture software for accounting purposes, a smart type of optical reader can process the information it gathers. Simply put, smart OCR understands the data it extracts regardless of the varying layouts and templates of the invoices. 

    Such an optical reading technique works with the help of artificial intelligence. This technology has been taught to recognize and discern invoicing information without any manual input such as with the introduction of rules. Essentially the equivalent of handsfree AP processing, business owners can even set up workflows processed by machines automatically to save time and resources. 

    With an automated invoice scanning and data capture system, you can eliminate redundant positions. However, having a manual reader to check the accuracy is vital for ensuring the smooth operation of this system.

    Structured And Semi Structured Documents

    In the world of big data, there are three types of files: structured, semi-structured, and unstructured.

    • Structured data refers to documents with elements and templates that are identical in appearance and structure for appropriate analysis. Examples include the fields in a excel file with a well-structured layout of rows and columns 
    • Semi-structured data includes invoices as they have a mixture of standard and unique elements that vary based on the template and content. A strong example is invoices consisting of constants such as data, or total amount and variables like costs, items, additional charges and others. By using a template-based system, you might waste resources defining rules for each. On the other hand, a smart OCR solution can decode the data intuitively and accurately 
    • Unstructured data such as Word, Text, and PDF files aren’t created in a predefined manner

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    Scanning invoices with an optical character reader to extract and collect data for analysis is the process of invoice data capture. Thanks to the applications of OCR, the drudgery that goes into manual capture of billing information can be dramatically reduced. 

    Based on the structure of your data, you can set up bill-capture with template-based or smart OCR. The former relies on manual approvals while the latter is an automated system tailored for semi-structured data for saving time and resources.