Text Detection and OCR with Google Cloud Vision API

2022-03-31 · Source: Adrian Rosebrock, Author at PyImageSearch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This lesson details how to perform text detection and Optical Character Recognition (OCR) using the Google Cloud Vision API. It covers the process of obtaining API keys and a JSON configuration file from the Google Cloud admin panel, configuring a Python development environment with necessary packages like `opencv-contrib-python` and `google-cloud-vision`, and implementing a Python script (`google_ocr.py`) to interact with the API. The script loads an input image, sends it as a byte array to the Google Cloud Vision API for OCR, and then processes and displays the detected text and its bounding boxes using OpenCV. The article demonstrates the API's accuracy on various images, including warning signs, challenging low-quality text, and street signs, noting its default word-by-word OCR output.

Key takeaway

For AI Engineers and Machine Learning Engineers building OCR-dependent applications, consider the Google Cloud Vision API for its high accuracy and ease of integration within the Google Cloud Platform ecosystem. Your choice of cloud OCR API should align with your existing cloud infrastructure (e.g., GCP for Google Vision API, AWS for Rekognition) to streamline data storage, compute, and overall application architecture, rather than solely focusing on API-specific code complexity.

Key insights

The Google Cloud Vision API offers high-accuracy OCR with minimal code, requiring a JSON key for authentication.

Principles

• Cloud platform ecosystem dictates API choice.
• Authentication via JSON key simplifies API access.

Method

Obtain Google Cloud Vision API JSON credentials, configure a Python environment with `google-cloud-vision`, load an image as bytes, send it to the API's `text_detection` function, and parse the `response.text_annotations` for OCR results and bounding boxes.

In practice

• Use `pip install google-cloud-vision` for setup.
• Process images as byte arrays for API submission.
• Integrate with OpenCV for result visualization.

Topics

• Google Cloud Vision API
• Optical Character Recognition
• Text Detection
• Cloud APIs
• Python Programming

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Adrian Rosebrock, Author at PyImageSearch.