Cataloging my video game collection using the OCI AI Vision Service 🎮

One of my hobbies is collecting video games, specifically retro games from the 80s and 90s.

I’ve previously used Azure Cognitive services (now Azure AI Services) to catalog my video game collection and wrote about it here 🕹️, as I’ve been playing around with OCI recently I thought it would a great idea to try and replicate this approach using the OCI AI Vision service – specifically the OCR capabilities that can extract text from an image.

Before I go any further, here is a little background to my specific scenario:

My games collection has grown over the years and it’s difficult for me to track what I own. On more than one occasion I’ve bought a game, later to realise that I already owned it 🤦‍♂️. I had a brainwave (or more specifically an excuse to tinker)……..why don’t I keep a digital list of the games that I have!

My plan was to take photos of my collection, pass these photos to the OCI AI Vision service to extract the text from the photos and then write this to a file, which I’ll then eventually put into a database or more likely Excel!

I took a photo of some of my games (PS3 so not exactly retro!) and then set about writing a Python script that used the Python SDK for OCI to submit the photos and extract any detected text, below is an example image I used for testing 📷.

The script that I wrote does the following:

• Connects to the OCI AI Vision Service
• Submits an image (which is stored within an OCI Object Storage Bucket) to the Vision Service for analysis, requesting OCR (denoted by the featuretype of TEXT_DETECTION) – you could pass a local image instead if needed, further details on how to do this can be found here.
• Converts the response from the AI Vision Service to JSON (which makes it easier to use)
• Output’s each line of text detected to the terminal, but only if this is greater than 5 characters in length – this helps to ensure that only game titles are returned, rather than other information on the spine of the box, such as the game classification and ID number.

This worked really well, with only a couple of small issues (can you spot them 🔎):

The script I wrote can be found below and also on GitHub.

import oci
import json

# Authenticate to the OCI AI Vision Service
config = oci.config.from_file()
ai_vision_client = oci.ai_vision.AIServiceVisionClient(config)

# Set the type of analysis to OCR
featuretype = "TEXT_DETECTION"

# Analyse the image within object storage
analyze_image_response = ai_vision_client.analyze_image(
    analyze_image_details=oci.ai_vision.models.AnalyzeImageDetails(
        features=[
            oci.ai_vision.models.ImageClassificationFeature(
                feature_type=featuretype,
                max_results=300)],
        image=oci.ai_vision.models.ObjectStorageImageDetails(
            source="OBJECT_STORAGE",
            namespace_name="Replace with Object Storage Namespace",
            bucket_name="Replace with the bucket name",
            object_name="Replace with the name of image to analyse"),
        compartment_id="Replace with Compartment ID"),
   )

# Convert to JSON
json = json.loads(str(analyze_image_response.data))

# Print the names of the games identified (each returned line in the resnpose with greater than 5 characters)
lines = []
for analysedlines in json["image_text"]["lines"]:
    if len(analysedlines["text"]) > 5:
        print(analysedlines["text"])
        lines.append(analysedlines["text"])