In today's data-driven world, the efficiency and the quality of data extraction are crucial. However, achieving this accuracy can be a challenging task due to a multitude of factors that can influence the quality of data obtained from these documents.

Let’s explore the intricacies of data extraction from receipts and invoices, shedding light on the various factors that can impact its accuracy. From the clarity of printed text to the layout of the document, and from the quality of scanning equipment to the request parameters used for api calls to submit a document. We will delve into the key elements that can make or break the accuracy. The information below can also serve as a self-debugging guide to users who have issues with data extraction.

For clarity let's break down the factors into two groups: Indirect and Direct.

Indirect causes are usually associated with document or image quality. Checking image or scan quality is the first step in the debugging process and understanding the root cause of extraction errors.

Poor Document quality can result in:

Misread Characters: Blurry or distorted images can cause OCR to misinterpret characters, leading to incorrect text extraction. This can result in wrong numbers, dates, or words being extracted from the document. e.g the constant case with OCR extracting 7 as 1 or 5 as 3.

Incomplete Extraction: If parts of the text or information are too faint or unclear due to poor quality, OCR might miss extracting those portions altogether. This can lead to incomplete or missing data in the final output. e.g the case with OCR extracting total 533 as 53 (as last digit could be unclear in the image)

Data Corruption: Poor image quality can introduce artifacts or noise in the image, which can be misinterpreted by OCR as valid characters or symbols. This can corrupt the extracted data.

Invalid Entries: OCR might extract gibberish or meaningless text from poor-quality images, especially if the image contains smudges, stains, or illegible ink. This can result in invalid or nonsensical data entries. e.g the case with bleed-though text interfering with line item description.

Samples for Bleed through text:

Misplaced Information: Blurriness, skew, or distortion can cause OCR to misplace extracted information. For example, a blurred decimal point might lead to incorrect amounts being extracted, as OCR might either miss it completely or extract it “dot” vs “comma”.

Loss of Context: Poor-quality images might lack the visual context required for accurate interpretation. This can affect the correct identification and extraction of important details such as transaction dates, vendor names, or item descriptions.

False Positives and Negatives: Poor-quality images can lead to false positives (incorrectly identified information) and false negatives (missed information). This can have cascading effects on downstream processes that rely on accurate data.

Please note, that file compression tools reduce the image size and thus can affect the data extraction accuracy.

In the JSON response from Veryfi, there are specific indicators that offer insights into the document's quality and whether you can fully trust the extraction results:

Out-of-the-box signals

Build your own document trust score

These indicators serve as valuable tools for assessing document quality and the reliability of extracted data, helping you make informed decisions about the usability of the extraction results.

The first thing to do when you review a document that has an error in data extraction is to ensure the image/document quality is suitable for accurate data extraction.

If the underlying issue affecting data extraction is, indeed, related to accuracy and the deskewing algorithms used by Veryfi, regrettably, there are limited actions we can take. Instead, we recommend the following steps:

Educate Your End Users

Emphasize the significance of providing high-quality scans and documents to your end users. Share Veryfi's best practices for capturing documents.

Here are a few tips on how to achieve great image quality

Implement Quality Alerts

Set up a logic on your side that could detect and flag poor-quality submissions based on the signals returned by Veryfi.

This proactive approach can help prevent inaccurate data extraction and allow for necessary corrective actions to be taken. By following these recommendations, you can enhance the quality of data submissions, mitigate accuracy issues, and ultimately optimize the performance of your data extraction processes.

There are some parameters and account settings on the user side that control the way the data extraction process is being handled.

Request Parameters
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Request parameters are data sent along with the request to the API Veryfi. These parameters provide additional information to Veryfi, allowing it to understand and process the request correctly. Request parameters are typically included in the URL or the body of an HTTP request.

User account settings, such as country, currency, categories list, vendor list, can indeed affect the data extraction process in various ways. User account settings are part of the so-called mask model which could provide context to the data extraction process.

Currency / Country / Region

Each account has both options to set a) a default currency and enable or disable auto-currency detection and b)set the country& region.
By default, if auto-currency is not enabled for your user account Veryfi will overwrite the extracted currency to a default one. Also if Veryfi could not find/identify the currency from the document, user account context such as currency and country will be used as a last resort. Profile settings link.
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Categories
Veryfi OCR API utilizes smart categorization at both the document and line item levels.

All Veryfi user accounts are being created with a standard list of Expense Categories (COA) that users can customize. Users can both modify the existing list of Categories in their user account or users can send along a list of categories for Veryfi model to pick up the best choice. Read more about Categories

Vendors

Every new vendor Veryfi extracts is being recorded in your Veryfi account Vendors section. If you struggle with vendor extraction you can add or create a new vendor via web portal and next time the model is hesitant about vendor extraction, as a last resort list of your vendor accounts will be taken to match against. Learn more about how to add new Vendor in web portal.

If image/document quality is not the root cause, please:

Direct Causes are usually tied to data representation and supported/unsupported document type.

Supported/unsupported document type as well as fields maturity and language support.

Veryfi has at least 7 public Data Extraction APIs:

While it is clear with W-2, W-9, Bank Statements, Contracts, and Bank checks as they have more or less conventional format and expected context, Receipts&Invoices API supports a great variety of document types in this category.
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A myriad of documents out there and for some support could be more confident for some less confident.

For example, high confidence could be expected for
Invoices like: Standard Invoices, Service Invoices, Credit Invoices, and Commercial Invoices; Purchase order numbers, Blanked Purchase orders, Contract Purchase orders,
Receipts like: Retail Point of Sale Receipts, Travel Expense Receipts, Invoice Receipts

Slightly less confidence for
Invoices: Proforma invoices, Recurring Invoices, Debit Invoices, Interim Invoices, Freight Invoices, and Expense Reports; Purchase order: Advance Purchase Order, Single-use Purchase Order, Recurring Purchase Order, Drop Ship Purchase Order. Bills: Power, Electricity, Garbage, Insurance and Property Tax Bills
Receipts like: Rental receipts, Utility Bill receipts, Service Receipts, Medical expense receipts Donation receipts, Online Order confirmations, Tickets or Reservation receipts.

Minimal support could be for:
Invoices like: Consolidated Invoice and E-invoices could come in various formats, such as XML or EDI, Bill of Landing, Packing Lists

Note that the document types above and confidence are checked against 170+ fields Veryfi extracts for Receipts/Invoices API. Depending on your specific case documents with minimal support could perform with 100% accuracy based on simple fields set and vice versa.

The model is confident in extraction results for frequent vendors representing particular domains & industries involved. For low-volume cases, extraction results may not be stable until confidence gained by providing more data for similar cases. Each domain/industry/vendors may have its own unique challenges, formats, and terminology.

Confidence in Frequent Vendors:

Challenges of Low-Volume Cases:

Gaining Confidence Through Data:

Unique Challenges, Formats, and Terminology:

Continuous Learning and Adaptation:

The model's confidence in data extraction results is closely tied to the frequency of encounters with specific vendors, domains, and industries. While it may exhibit high confidence for common cases, it may require more exposure to low-volume or unique cases to achieve stability and accuracy.

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It is also possible that the model has a vast amount of data representation for a particular case but needs some fine-tuning training on how to extract the data accurately. This is where model training can be beneficial. Based on the volume and information provided, we can customize the training to improve accuracy based on your project requirements. In the case of low-volume vendors, there may be ample feedback available to improve extraction accuracy. However, for frequent vendors, there might be a lack of specific feedback in this particular field, causing the model to be unaware of the correct extraction method.

During model training sessions, all the changes made by users to processed documents are incorporated. This means that any corrections, updates, or feedback provided by users directly contribute to the refinement and enhancement.

Whenever you encounter any inaccuracies or errors in the extracted data, you can correct them using the PUT, POST, and DELETE operations provided by Veryfi's API. By making these corrections, you provide valuable feedback that helps train and refine the Machine Learning models.

Please reach out to our support team at support@veryfi.com. Provide us with comprehensive details and context regarding the problem.

By collaborating and exchanging data from both ends, we can expedite the process of enhancing accuracy.

Our goal is to ensure that Veryfi consistently delivers accurate results, and we appreciate your proactive engagement in helping us achieve this objective.

Please, reach out to support@veryfi.com if you have any questions.