AI Image Recognition in Mobile Apps

What image recognition can do inside a working app

Most image recognition requests Tepia receives are not about novelty. They are about removing typing and judgment calls from a task people do with a phone in one hand. A camera can handle a lot of that work when the app is built for it:

• Identify: recognize a part, product or piece of equipment, such as a replacement filter, a fastener type or an appliance model, and pull up its record.
• Read: extract text from data plates, serial number stickers, meter faces, shipping labels and handwritten tags.
• Detect: spot and outline damage, corrosion, leaks, missing components or safety hazards in a photo.
• Count and measure: tally items on a shelf or pallet, or estimate dimensions against a reference object.
• Verify: confirm a proof of work photo shows what it should, is in focus and was taken at the job site.

The common thread is a decision that used to depend on someone’s experience or patience. A new technician who photographs an unfamiliar data plate gets the model, serial number and manufacture date filled in. A warehouse associate gets a count instead of a clipboard. Tepia scopes recognition around those moments in your workflow.

Choosing the recognition approach

There are four realistic ways to add recognition, and Tepia tests the likely candidates on your own photos before committing:

Many apps combine two. A common Tepia pattern runs a small on device model for live framing and quality checks, then sends the confirmed photo to a cloud model for detailed recognition. For a closer look at running models on the phone, see on device AI for mobile apps.

Training data and labeling

Pretrained models know general categories well and your catalog barely at all. Recognizing your 300 part numbers, your defect types or your packaging means training on your own photos. Tepia plans for that from the start, because the data question shapes the timeline more than the modeling does.

The usual path looks like this:

1. Gather existing photos from job records, product listings and inspection reports, and check them for quality and variety.
2. Define the classes or defects with your experts, including look alike items that must be told apart.
3. Label a starter set in a simple review tool, with two people checking the hard cases.
4. Train, then test on photos the model has never seen, taken in real lighting by real users.
5. Ship with a correction flow so every user fix becomes a new labeled example for the next version.

When photos are scarce, Tepia often launches with a multimodal model or a cloud service and switches to a custom model once enough labeled images accumulate. If your images first need sorting and cleanup, see data preparation for AI projects.

Designing the camera experience

Recognition accuracy depends as much on the photo as on the model. Tepia designs the capture screen to get a good image every time: an overlay showing where to frame the label or part, live checks for blur, glare and darkness, a prompt to turn on the flashlight, and automatic capture once the frame is steady. A few seconds spent here prevents most failed recognitions.

Results need equally careful design. Tepia shows the top match with its confidence, alternates when the model is unsure, and a quick path to search or type manually. Recognized text appears in editable fields, never as a locked value. Every photo keeps its timestamp and location, which supports proof of work and audit needs, and our job photo, signature and proof of work app page covers that side in detail.

Privacy deserves attention too. Job photos often catch faces, house numbers, license plates or documents on a desk. Tepia can blur faces and plates before upload, strip location metadata from images shared outside your team, and keep raw photos in your own cloud storage rather than a vendor’s. When a cloud vision service or model provider is used, Tepia confirms its retention terms so your images are not kept or used for training.

How Tepia approaches AI image recognition

Tepia’s six phases put real photos at the center of every decision. A US based project manager coordinates your field experts and Tepia’s engineers.

1. Discovery: field observation of how photos are taken today, collection of a sample photo set, and a quick comparison of recognition approaches on those images. Deliverables are an Investigation Summary with accuracy findings, an Interview Summary and User Stories such as an associate identifying an unlabeled part.
2. Design: wireframes for the capture overlay, quality warnings, results and correction screens, tested outdoors and in dim rooms.
3. Development and Testing: labeling, training and app development in parallel, with an Alpha on internal devices and a Beta in the field. Test plans measure accuracy on unseen photos, speed on older phones and behavior offline.
4. Training: hands on sessions on capturing good images, reviewing results and correcting matches.
5. Launch: App Store and Google Play release, migration of reference catalogs and a staged rollout by team.
6. Support: periodic retraining with corrected images, new classes as your catalog grows, and issue resolution.

Plan on Discovery taking 1 to 3 months and development and QA 2 to 6 months. See our work, our process, or contact Tepia.

Frequently asked questions