OCR Lot Code and Expiry Date Verification for Medical Packaging

Medical device recalls hit a four-year high in 2024 with 1,059 events recorded in the US, and roughly 25% trace back to mislabeling of batch numbers and expiry dates on packaging. A new vision AI pipeline using Roboflow Workflows automates the detection and validation of these critical fields by training a localization model to find batch and expiry strips, reading the text with Google Gemini, and running format and expiry validation automatically. The system is designed to catch errors that human inspectors miss at line speed, and it can be adapted for surgical kit pouches, IVD reagent boxes, and implant labels across the medical packaging industry.

Automatically find batch and expiry strips, read them, and auto-validate lot codes and dates on medical packaging lines with this vision AI pipeline. Pharmaceutical packaging lines print batch numbers and expiry dates on every pack that leaves the line. A wrong digit, a smudged field, a date that never printed correctly. Those get through manual inspection more often than they should. Medical device recalls hit a four-year high in 2024 https://meddeviceguide.com/blog/medical-device-recall-trends-2024-2026-statistics-root-causes-guide?ref=blog.roboflow.com , with 1,059 events recorded in the US alone. Roughly . The stakes are the same across the industry. https://www.qualityze.com/blogs/medical-device-recall-management-guidelines?ref=blog.roboflow.com 25% trace back to mislabeling Catching these on a packaging line is harder than it sounds. Batch and expiry fields sit in different positions across label layouts, ink density shifts between printhead passes, and some strips run vertically while others run horizontally. A human checker moving at line speed is going to miss some. This guide walks through building a print verification pipeline in Roboflow Workflows https://roboflow.com/workflows/build?ref=blog.roboflow.com . You'll train a localization model to find the batch and expiry strip, crop it, read the text with , and run format and expiry validation automatically. https://roboflow.com/model/google-gemini?ref=blog.roboflow.com Google Gemini This pipeline is not limited to pharmaceutical packaging. Surgical kit pouches, IVD reagent boxes, and implant labels all carry the same fields. Swap the dataset, retrain the localization model on your label layout, and this Workflow carries over unchanged. OCR Lot Code and Expiry Date Verification for Medical Packaging Go to Roboflow Universe https://universe.roboflow.com/?ref=blog.roboflow.com and search for the . Roboflow Universe hosts over 250,000 open source datasets across industries. https://universe.roboflow.com/college-hxirw/major-project-8anow?ref=blog.roboflow.com major project dataset This dataset has 1,716 images of pharmaceutical packaging with two annotated classes: name for the product label region and date for the batch and expiry strip. The variation covers ink density shifts, vertical and horizontal strip orientations, and partial occlusion from fold overlap. That is what makes your localization model robust before it sees a single production image. Click Fork Dataset to copy it into your workspace with all annotations intact. Build the Workflow Here is the workflow we will build https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiblhnWVJVWHdJc1g2OEY5V3NEQTciLCJ3b3Jrc3BhY2VJZCI6Im5JRk5DOGRjbU5OOXZ4d29ybWpoWTdCNjdQZTIiLCJ1c2VySWQiOiJuSUZOQzhkY21OTjl2eHdvcm1qaFk3QjY3UGUyIiwiaWF0IjoxNzgxMDkyNjUzfQ.y4GQ3DMH1BILeNwCaQKX3o05xW1J3KDASm-XANsjy0w?ref=blog.roboflow.com . Before building, here is what each block does and why it is in the chain. Image Input: entry point for every image Object Detection Model: locates the batch and expiry strip on the full image Detections Filter: passes only date class detections downstream Dynamic Crop: isolates the strip as a tight crop Google Gemini: extracts batch number and expiry date from the crop Gemini Result Parser: makes both fields addressable Python Validation: checks format and expiry date, returns PASS or FAIL Bounding Box Visualization: draws detected regions on the output image Step 1: Train the Object Detection Model After forking the dataset, open it in your workspace, Select Custom Training to configure your model settings. Choose Roboflow RF-DETR https://rfdetr.roboflow.com/latest/?ref=blog.roboflow.com as your model architecture. RF-DETR is Roboflow's real-time object detection model that delivers high accuracy with faster convergence, which makes it a strong fit for label region detection. Adjust the train/valid/test split. The default 80/10/10 works well here: 1,373 images for training, 172 for validation, and 171 for testing. Click Save to confirm your split. If you want to control how long the model trains, open Advanced Options before starting and adjust the number of epochs. Click Start Training . When training finishes, the model card shows mAP https://blog.roboflow.com/mean-average-precision/ , , https://blog.roboflow.com/precision-and-recall/ :~:text=Machine%20Learning%20Video-,What%20is%20Precision%3F%20What%20can%20we%20learn%20from%20Precision%3F,-Roboflow%20user%20trained Precision , and https://blog.roboflow.com/precision-and-recall/ :~:text=will%20waste%20water.-,What%20is%20Recall%3F%20What%20does%20Recall%20tell%20us%3F,-Let%27s%20imagine%20we Recall . This model achieved 91.2% mAP with 90.3% precision across both classes. https://blog.roboflow.com/f1-score/ F1 With your model trained, you are ready to build the Workflow. Step 2: Add the Object Detection Model Go to Workflows from the side panel and create a new Workflow from scratch. To add a block, click the black + in the canvas and search for the block by name. Under the Image section, connect inputs.image. This tells the block which image to run detection on. Under the Model section, paste your model identifier from the training page. You can find it on your model card after training completes. It looks like this: major-project-8anow-oqg51/2. Leave Confidence Mode on Best Recommended . This block forms the core of the pipeline, as every downstream step relies on the bounding boxes it produces. Step 3: Add the Detections Filter Click + and search for Detections Filter. Under Predictions, connect object detection model.predictions. Under Operations, click Edit and configure the filter to pass only detections where class equals date. This drops the name detections and sends only the batch and expiry strip downstream to Gemini. Without this block, Gemini receives crops from all detected regions, including the product name, which returns no useful batch or expiry data. Step 4: Add Dynamic Crop Click + and search for Dynamic Crop . Under Image to Crop , connect inputs.image. Under Regions of Interest , connect detections filter.predictions. Leave Mask Opacity at 0 and Background Color at 0,0,0. This block cuts the detected batch and expiry strip out of the full packaging image and passes a tight crop to Gemini, giving it a clean region to read from. Step 5: Add Google Gemini Click + and search for Google Gemini . Under Image , connect dynamic crop.crops. Set Task Type to Visual Question Answering . Under Prompt , paste the following: Extract batch number and expiry date from this cropped label. Return ONLY a valid JSON object with exactly these two fields: {"batch number": "", "expiry date": ""}. Do not include markdown, explanation, or extra text. Gemini reads the printed text directly from the crop and returns a clean JSON object with both fields. No OCR model training required. Step 6: Add the Gemini Result Parser Click + and search for Gemini Result Parser . Under gemini json string , connect google gemini.output. Click Edit Code and paste the following: python def run self, gemini json string : import json, re raw = str gemini json string or "" clean = re.sub r" json| ", "", raw .strip try: data = json.loads clean except: m = re.search r"\{. \}", clean, re.DOTALL data = json.loads m.group 0 if m else {} batch = str data.get "batch number" or "" .strip expiry = str data.get "expiry date" or "" .strip return { "batch number": batch, "expiry date": expiry, "parsed results": {"batch number": batch, "expiry date": expiry} } This strips any markdown formatting that Gemini occasionally adds and returns batch number and expiry date as clean addressable fields. Step 7: Add the Python Validation Block Click + and search for Python Block . Under parsed results , connect gemini result parser.parsed results. Python Validation Click Edit Code and paste the following: python def run self, parsed results : import re from datetime import datetime data = parsed results or {} batch number = str data.get "batch number", "" or "" .strip expiry date = str data.get "expiry date", "" or "" .strip batch valid = bool re.match r"^ A-Za-z0-9\-\.\/ {4,}$", batch number fmt1 = re.match r"^ JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC \.\d{4}$", expiry date.upper fmt2 = re.match r"^ 0 1-9 |1 0-2 /\d{4}$", expiry date fmt3 = re.match r"^\d{4}: 0 1-9 |1 0-2 $", expiry date expiry format valid = bool fmt1 or fmt2 or fmt3 expiry valid = False if expiry format valid: try: if fmt1: exp = datetime.strptime expiry date.upper , "%b.%Y" elif fmt2: exp = datetime.strptime expiry date, "%m/%Y" else: exp = datetime.strptime expiry date, "%Y:%m" exp end = datetime exp.year + 1, 1, 1 if exp.month == 12 else datetime exp.year, exp.month + 1, 1 expiry valid = exp end datetime.today except Exception: expiry valid = False status = "PASS" if batch valid and expiry valid else "FAIL" return { "status": status, "results": { "batch number": batch number, "expiry date": expiry date, "batch valid": batch valid, "expiry format valid": expiry format valid, "expiry valid": expiry valid, "status": status } } The block validates the batch number as an alphanumeric string of four or more characters and accepts three expiry date formats: MMM.YYYY, MM/YYYY, and YYYY:MM. If the expiry date is in the past the pack returns FAIL regardless of format. The comparison runs against datetime.today , so the result always reflects the current date when the Workflow runs. Step 8: Add Bounding Box Visualization Click + and search for Bounding Box Visualization . Under Input Image , connect inputs.image. Under Predictions , connect object detection model.predictions. Leave Color Palette on DEFAULT . Step 9: Configure the Outputs Click the Outputs block and add three outputs: - output image pointed at bounding box visualization.image - validation results pointed at python validation.results - predictions pointed at object detection model.predictions Click Save . Your Workflow is ready to run. Step 10: Test in the Workflow Editor Open the Workflow editor and upload a test image from your forked dataset. Click New Run to run the pipeline. Your pipeline is fully wired and running. Head to the Results section to see what comes out. Workflow Results Test case 1: Clean label, status PASS A pharmaceutical packaging box with a clearly printed batch and expiry strip. The localization model detects the region, Gemini extracts both fields, and the validation block confirms the batch number is valid and the expiry date is in the future. Both fields present, format correct, expiry date valid. The pack clears verification. Test case 2: Expired product, status FAIL A sterile medical device pouch with a lot and expiry strip printed on the right edge. The localization model detects the strip, Gemini extracts both fields, and the validation block correctly flags the product as expired. The batch number passes validation, but the expiry date is over five years in the past. The pack fails verification before it reaches the next stage. Automate OCR Lot Code and Expiry Date Verification with Roboflow Agent Want more help? You could also just describe the problem you want to solve to Roboflow Agent in plain language and it creates the workflow for you. Watch the video below to see the Agent assemble the pipeline from prompts. OCR Lot Code and Expiry Date Verification for Medical Packaging Conclusion Medical device manufacturing https://roboflow.com/industries/manufacturing?ref=blog.roboflow.com lines run under strict traceability requirements. Surgical kit pouches, IVD reagent boxes, implant labels, and sterile barrier packaging all carry lot numbers and expiry dates. The same Workflow you built handles this without any structural changes. What changes is the dataset. Fork or build a dataset of your specific label layout and retrain the localization model on your date and name regions. Label positions vary across device types so the model needs to learn your layout. Everything downstream in the Workflow stays identical. For MES https://blog.roboflow.com/manufacturing-execution-system/ or quality system integration, pass validation results directly to your batch record database at the point of packaging. A FAIL status triggers a line stop before the pack moves to sterilization or final packaging, with no manual review step. If your label carries a UDI field, extend the Gemini prompt with no retraining required: Extract batch number, expiry date, and udi from this cropped label. Return ONLY a valid JSON object with exactly these three fields: {"batch number": "", "expiry date": "", "udi": ""}. Do not include markdown, explanation, or extra text. The Python validation block then adds a UDI format check alongside the existing batch and expiry logic. Further reading: Cite this Post Use the following entry to cite this post in your research: Mostafa Ibrahim /author/mostafa/ . Jun 11, 2026 . OCR Lot Code and Expiry Date Verification for Medical Packaging. Roboflow Blog: https://blog.roboflow.com/ocr-lot-code-and-expiry-date-verification/