Real-Life OCR Challenges for Arabic ID Processing

While modern OCR technology can achieve high accuracy on Latin-based documents, other scripts tend to pose a tough challenge. One of these scripts is Arabic—its many special linguistic features, such as diacritics and cursive, require careful handling by the system. Not only can these features be a problem themselves, they also tend to exacerbate problems relevant even to the Latin script.

Looking for more information? Read further to find out as we explore the linguistic and technical difficulties that arise when processing the Arabic script.

Reading Arabic text via the OCR technology is inherently difficult due to the nature of the script. Arabic letters are cursive—they connect to each other, and the shape of a letter changes depending on its position in a word (initial, medial, final, or isolated form). Letters that are distinct when isolated can look completely different when joined in a word, so an algorithm must learn many variant glyph shapes for each underlying letter. 

Moreover, not only is Arabic script written from right to left (RTL), many Arabic-speaking countries print personal data in Arabic alongside English or French on the same document. That’s why an OCR engine that is not optimized for bi-directional content might process text in the wrong order or even mix up fields. It’s also worth mentioning that, although Arabic words run RTL, numerals within Arabic text run left to right (LTR). This means that a date or ID number can also confuse parsing if the system expects uniform direction.

Arabic also includes many diacritical marks and dots that are essential to meaning. For instance, dots distinguish ب (b) from ت (t) or ث (th), and diacritic marks can indicate vowels or other pronunciations. These can present a problem, especially when printed small or scanned under less-than-ideal conditions, or if the ID scan is low resolution.

Finally, there is the use of ligatures—combinations of letters that merge into a single glyph (the classic example is “لا”, which represents lām + alif together). Arabic printing routines (including those on ID documents) frequently use such ligatures for common letter pairs.

Arabic naming conventions often include multiple components beyond the first name/last name. It’s common to see a person’s given name followed by their father’s name, grandfather’s name, and family name (surname). This way, an ID might show “MOHAMED ABDUALLAH JASIM ALI YASER”—which in a Western context could be parsed in various ways. If a form or database expects only two name fields, it may be unclear which parts constitute the surname.

Another problem is transliteration inconsistency. Arabic script must be transliterated into Latin letters for use in international systems (e.g., passenger manifests, credit bureaus), but there is no single universally agreed-upon transliteration standard. Different countries and organizations use different rules, and individuals’ names can be spelled in many ways when converted to English. A very common name like "محمد" can be seen as “Mohammed,” “Muhammad,” “Mohamed,” or “Mehmet,” or even abbreviated “Mohd”. All refer to the same Arabic name, but an automated check might not realize they are equivalent.

Automated checks cannot do a one-to-one match in such cases. Instead, verification algorithms may perform partial comparisons, trying to match the portions that are present. That makes the entire process very nuanced—if the settings are too strict, the system may flag legitimate IDs; if they are too loose, it might accept mismatches.

Regardless of language, the image capture conditions and document design still play a massive role in OCR accuracy. For Arabic-language IDs, this is especially true because of the fine details. Several technical aspects must be considered:

ID cards are often laminated or made of plastic, which means they can produce glare under camera flash or overhead lights. This can obliterate portions of text on an image: a shiny spot on the ID might white-out the dark text beneath. Similarly, uneven lighting or shadows can make parts of the image too dark to read. 

In the context of Arabic text, glare that washes out one dot of a letter can turn a ق into a ف, for example. That’s why it’s considered best to capture ID images in a well-lit environment with diffused light, avoiding direct light that hits the card at an angle that reflects into the camera.

A blurry image can merge distinct letters, or it can cause the OCR to miss thin lines (like the dotless letter س with its fine teeth). High resolution is especially important for small text like dates or ID numbers, which on cards might be printed in a small font.

Motion blur from an unsteady hand is another common issue, as it can double-edge the text or make it fuzzy. This again emphasizes the importance of user guidance: they must hold the camera steady, and use camera stands or alignment rigs if possible.

It’s crucial that the entire ID card is within the frame, and not rotated at an extreme angle. If a portion of a name or number is outside the photo, the OCR obviously can’t read it. Likewise, if the card is captured at a slant, the text lines may appear skewed or perspective-distorted (where one side of the card is larger). 

The best practice is to have the camera directly above the document and the ID aligned straight. Many systems nowadays provide feedback like edge detection or an on-screen rectangle to guide users to position the ID correctly.

Modern IDs incorporate security elements that sometimes inadvertently hinder OCR. One example is holographic overlays: many cards have a hologram sticker or laminate that, under certain light, shows a reflective pattern on top of the printed information. These holograms are known to confuse OCR, as they might appear as random shapes or noise on the text in the image. Similarly, ghost images, guilloché background patterns, microprinted text, or UV markings can all reduce the contrast or add clutter for the OCR software. 

This is why high-end document readers use multiple light sources (visible, infrared, and ultraviolet) and take multiple images, which the software then analyzes. However, for a mobile OCR solution, you may have to rely on a single RGB image, so it’s all about optimizing how that image is captured to minimize security feature interference.

Lastly, IDs can be scratched, scuffed, faded, or have stains; any such damage will impact OCR. And in the case of Arabic, a scratch across a word might remove one or more dots or even whole letters. Moreover, dirt or smudges can look like false strokes, potentially causing false readings. 

Cleaning the ID surface before scanning (if possible) and ensuring it’s in decent condition helps. However, since you often cannot control how worn a user’s ID is, OCR software must be as robust as possible to avoid these issues. Image enhancement and adaptive thresholding are some of the methods used to cope with scratches or low-contrast text.

Given the above challenges, it is practically impossible to have a solution that will work flawlessly under any conditions, especially in the case of Arabic. The best results in this case will come from a combination of an advanced OCR engine (with support for Arabic) as well as an extensive document template library to help the engine interpret the fields correctly.

Regula provides both parts of the solution: Regula Document Reader SDK supports over 138 languages (including Arabic) and more than 600 data field types, while our template database is the biggest in the world, with 15,000+ documents from 252 countries and territories.

The solution’s lexical analysis allows for transliteration of data from non-Latin into Latin, as well as cross-validation of the data fields. Moreover, its neural networks are highly trainable and adaptable; the more IDs it processes, the closer its accuracy is to being perfect—even with the Arabic script.

With Regula Document Reader SDK, you will be able to:

• Authenticate thousands of ID documents from all over the world, including Arabic-speaking regions.

• Read machine-readable zones (MRZs) and barcodes.

• Read and authenticate RFID chips. 

• Verify digital signatures encrypted into barcodes using the ICAO Datastructure format.

• Verify dynamic security features, including holograms and optically variable ink (OVI).

• And more.

Let’s break through barriers—together. Book a call to learn more about our solutions!