The Impact of Image Annotation on Banking Services

Ever consider using your smartphone's banking app to deposit a check? All you have to do is snap a photo, and the bank’s software processes the text, numbers, MICR code, etc., it can even recognize the difference between the printed information, the check's orientation, and the handwritten signature.

The process seems simple, but it is fueled by the image annotation method millions of Americans use today for mobile check deposits. This type of banking service demonstrates how far technology has advanced in its ability to comprehend and analyze visual information with ease.

In essence, image annotation provides the "ground truth" data that allows machine learning models to identify and read the amount, ensuring no duplication while making the seamless and secure mobile check deposit experience for people.

What is image annotation?

The process involves annotating images like photographs, where annotators add labels, tags, or comments with extra information that will subsequently be sent to computers to recognize items from image sources.

Image annotation services have been implemented in a variety of valuable ways. Banking is one area, but there are others, like healthcare, where patient data is carefully examined, and medical equipment is improved to become more predictive. AI and machine learning models are thriving because of training data to become more proficient at identifying details in images and videos.

The annotated images will act as the basis for a machine learning project, so creating precise, dependable training data is a prerequisite. A scalable computer vision application requires investment in high-quality picture annotation, making it more than just a best practice.

Case of bank check deposit and types of annotation used

Now that you know what image annotation means, we should get back to our initial example of mobile check deposit. You must have read all the guides and dos and don’ts of data annotations. However, many are unaware of the actual application of these training data and where the annotation methods have been applied. Here, we will discuss how the checks are processed and where the different types of image annotation methods have been applied.

Types of image annotation enabling mobile check deposit:

Bounding Box Annotation

Bounding boxes are a common way to demarcate objects in an image. It means the local selection of an object within a rectangular box. They are primarily required for precise annotations and are foundational to training object-detection models.

The role of bounding boxes for annotation tasks includes:

• For the entire check: A bounding box would be drawn to define the boundaries around the entire check.
• For MICR code: A separate bounding box would be drawn around the MICR code area.
• For the amount field: A bounding box would be drawn around the area where the check amount is written or printed.
• For the payee's name: A bounding box containing the payee's name will be drawn around the area.

An annotator is required to do the following:

1. The data annotators have to manually outline the specific fields like payee name, account number, amount, signature, etc., using annotation tools to draw bounding boxes.
2. The bounding box must be close to the bank’s check without clipping it.
3. Avoid overlapping of boxes with each other. They must not touch each other unless it truly occludes with the object.
4. Labeling should be consistent, as similar bank checks in all images must share the same label to ensure uniformity in model training.
5. A feedback loop is essential to review data to meet the banking model requirements.

Once annotations are completed, they are exported in formats like YOLO (.txt), COCO (.json), or Pascal VOC (.xml), as per the requirements of the project.

The bounding box annotations become necessary to build AI systems that are effective in object detection where precise annotations lead to best-performing models. Thus opening the way to more advanced and reliable AI-driven solutions.

Polygon Annotation

While it is difficult to trace the edges of objects in bounding boxes for precise annotation, polygon annotation makes this task easier. They can be used for marking irregular shapes and hence perfect for annotating complex or non-rectangular items.

The role of polygon annotation in the recognition of complex shapes includes:

• To annotate edges: Different checks are utilized in the training model, and the edges of the images are perfectly outlined using polygon annotation.
• For specific characters within the MICR code: Polygon annotation isolates individual characters within the MICR code for better recognition.

An annotator is required to do the following:

1. Maintaining annotation accuracy is essential to facilitate data extraction from the bank’s check and automate processing, which requires polygon annotation to plot multiple points around the check's boundary.
2. These points connect to form a shape that captures the exact contour of the object, such as the date written on the check for determining its validity.
3. Ensure the polygon fits tightly around the check without missing any parts or including irrelevant areas.
4. Consistency in labeling similar checks is vital to ensure uniformity across the dataset.
5. After completing the annotations, the data is reviewed through a feedback loop to correct any errors.

Finalized annotations are exported in formats like COCO or JSON, which are compatible with various AI model training pipelines.

The role of image annotation, combined with the use of polygon annotation, improves computer vision models' performance on tasks like segmentation and object recognition, particularly when high precision is required.

Semantic Segmentation

In semantic segmentation, a class label is assigned to recognize text even in challenging scenarios and detect anomalies at a pixel level. This is more than object-level annotations because each segmented area is assigned a specific label, and annotators ensure consistent labeling to avoid errors in data processing between various object types and the background.

The role of semantic segmentation includes:

• Precise recognition of each field in the check: To segment the predefined categories, such as MICR code, payee name, amount field, etc., by coloring the exact pixels corresponding to the region.
• Separate surrounding areas: The segmentation method accurately separates the check area from the surrounding parts or objects in the image.

An annotator is required to do the following tasks:

1. Mark each pixel as per the object class with the help of specialized annotation tools. For fields like the signature, which might overlap with the background or other text, annotators must carefully isolate the pixels to separate them from the surrounding areas.
2. To create a segmented map that defines and categorizes specific labels in the image. (e.g., amount in numbers, bank logo)
3. Annotate with high granularity, ensuring class boundaries are marked correctly, and no pixels are incorrectly labeled.
4. Human supervision is required to review details in annotation to ensure that they are of good quality and meet the project's requirements.
5. For extracting data from scanned documents, consistency must be maintained so that machine learning models can recognize handwritten or printed text in bank checks.

After ensuring proper annotation and quality checks, the images are to be exported in COCO or PNG formats.

The semantic segmentation method ensures high-quality datasets for developing complex models that can comprehend and analyze visual data in great detail. It also improves field extraction for automation systems, particularly for non-standard or multinational checks.

The bottom line

Since bank check data extraction has traditionally been a laborious and error-prone process. Now, however, technology is changing to create new products or breakthrough models. Information from checks is becoming more accurate and faster thanks to new techniques like image annotation for training machine learning models.

Processing deposits, confirming transactions, and auditing are just a few uses for the retrieved data. Image annotation improves artificial intelligence models that are incorporated into software and apps, such as one example we discussed above (bank check deposit).

Why Partnering with a Data Annotation Vendor Matters?

The annotation companies better understand the needs and specifications of image annotation projects. Thus, leveraging their experience is beneficial for data scientists because labeling an image is not just simply categorizing an image; it also entails adding additional information.

So, not only professional work or accuracy in work is needed, but outsourcing will also save the time and cost required to build an in-house team. For example, the annotation task for environmental monitoring may include adding tags to the tree's name, botanical name, soil and weather needs, ideal growing temperature, and more if the project requirements are intricate and call for sharing more details with the model.

That is why, before choosing image annotation services, you should be more careful about what your provider promises to deliver, the annotator's proficiency, and the annotation tools they use, among other considerations.

Conclusion

Image annotation for domain-specific computer vision models is the next big key technology trend, and the timing and caliber of the datasets your provider offers will determine how well your machine learning model performs.

The need for specialization is important here because an expert can annotate any information that helps machines better understand what is contained in an image. Moreover, this incredibly labor-intensive task requires several hours of manual labor.