Interpreting OCR and Barcode Match

Applies to Direct | Capture

When you compare barcode or MRZ data to OCR data, you want to know if the results indicate a potentially fraudulent form of identification. That determination is your responsibility. Intellicheck returns results. We do not return a decision. Our results are there to help guide your decision-making process:

• Barcode results are returned in idcheck
• MRZ results are returned in extendedId
• OCR results are returned in OCR

If you request OCR matching, we return an ocr_match object with the comparison results between OCR and idcheck in the case of a driver's license, or extendedId in the case of an international form of ID. In ocr_match, you'll find comparison results for more than a dozen fields, such as:

• Issuer name
• Cardholder name
• Date of birth
• Address

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You decide which OCR match fields are relevant to you based on your use case and business requirements.

Follow the authentication trail

To get a better idea of what the results are telling you, you can imagine the authentication happening as a workflow from the barcode to the OCR extraction to the front-back matching. At each step along the flow, evaluate the results to understand the risks.

Was the barcode decoded?

Start with the barcode. The barcode is fundamental. If the idcheck object returns a processResult of documentUnknown, you can consider the barcode suspicious.

In the same way, if extendedId returns a documentRecognized of 0, you can consider the international ID suspicious.

Was OCR successful?

Next, check the OCR object. The OCR results tell you if the document text was successfully converted to data based on the submitted image. In the case of a driver's license, the submitted image is of the front side.

If successful, all or some of the text is returned in the OCR object and the documentRecognized property is 1. Keep in mind that not all text is OCR'able. Some fields might be empty due to poor image quality. You can still get a documentRecognized of 1 with partial results.

If not successful, the OCR object is empty and documentRecognized is 0. If documentRecognized is 0, you can be cautious about the ID validity.

How well did OCR match the barcode?

Finally, see what matches. Matching results give insight about document consistency. But they also present a challenge because matches or mismatches can be due to poor image quality. Glare, blurriness, and dim lighting can cause OCR mismatches on an otherwise authentic ID. But mismatches can also point to discrepancies that hint at potential fraud.

As mentioned above, the ocr_match object returns more than a dozen match fields with the properties you might consider most relevant to your decision-making process, such as issuer name (isIssuerNameMatch), cardholder name (isNameMatch), and date of birth (isDobMatch).

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See OCR Match Details for more information.

Conceptual flow diagram

The following diagram illustrates the authentication flow described above.

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The diagram represents a conceptual, theoretical business-decision process. Intellicheck provides results only, not a decision regarding authenticity.

1. Notice how the flow starts with barcode results. We assume the barcode is okay. If not, you would consider the ID suspicious in this theoretical scenario.
2. Next, evaluate the OCR response. Did it pass? If not, then you have a case where the barcode passed but the OCR did not, and you have no OCR data with which to compare. This could be suspicious. But it could also be poor image quality. You should be cautious about the ID validity.
3. Finally, look at the OCR match details. You already have a good barcode and a successful OCR response. Keep that in mind when you consider the matching results:

• All matched. If all fields matched, you can be confident. In this scenario, you decide that the likelihood of fraud is very low.
• Some matched. If some of the relevant fields matched but others did not, you should be cautious about the ID validity. Keep in mind that we return OCR match results for more than a dozen fields. You decide which ones are most relevant for your use case and business requirements. For example, you might prioritize issuer, date of birth, and expiration date. These fields weigh heavily in your decision. Partial matches can be caused by poor image quality. Partial matches can also hint at fraud. Review OCR Match Details for information about matching thresholds and scores for individual fields.
• All mismatched. If all of the fields mismatched, you should be suspicious about the ID validity. In this scenario, it appears that you have a driver's license with a good barcode and a recognizable front image. But all data is mismatched.