Update license_plate_ocr.ipynb copy text (#179)

* Update license_plate_ocr.ipynb Updated copy. Signed-off-by: Bianca Ragsdale <[email protected]> * Update license_plate_ocr.ipynb Signed-off-by: Bianca Ragsdale <[email protected]> * Update license_plate_ocr.ipynb Fixed line breaks. Signed-off-by: Bianca Ragsdale <[email protected]> --------- Signed-off-by: Bianca Ragsdale <[email protected]>
landing-ai · Feb 16, 2024 · 540d6ed · 540d6ed
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commit 540d6ed
Showing 1 changed file with 5 additions and 5 deletions.
diff --git a/examples/license-plate-ocr-notebook/license_plate_ocr.ipynb b/examples/license-plate-ocr-notebook/license_plate_ocr.ipynb
@@ -12,17 +12,17 @@
     "3. [Image Cropping](#image-cropping)\n",
     "4. [OCR and Data Retrieval](#ocr-and-data-retrieval)\n",
     "\n",
-    "In this Jupyter notebook, we guide you through a comprehensive process of building a computer vision application with LandingLens focused on detecting and reading license plates from videos. Starting with frame extraction, followed by the detection and cropping of license plates, and finally, Optical Character Recognition (OCR) for data retrieval, each section is crafted to provide you with a conceptual understanding and practical code examples. By the end of this notebook, not only will you have a functioning license plate reader, but you'll also possess foundational knowledge and techniques that are transferable to a myriad of other computer vision applications. Whether you're aiming to recognize faces, track objects, or read text from images, the principles and methods showcased here will serve as a valuable cornerstone for your future projects."
+"In this Jupyter notebook, we guide you through the comprehensive process of building a computer vision application with LandingLens that detects and reads license plates from videos. Each section is crafted to provide you with a conceptual understanding of the step in the process and practical code examples. The process starts with frame extraction, followed by the detection and cropping of license plates, and finally, optical character recognition (OCR) for data retrieval.\n\n By the end of this notebook, not only will you have a functioning license plate reader, but you'll also possess foundational knowledge and techniques that are transferable to a myriad of other computer vision applications. Whether you're aiming to recognize faces, track objects, or read text from images, the principles and methods showcased here will serve as a valuable cornerstone for your future projects.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Installation and setup\n",
+    "## Installation and Setup\n",
     "\n",
     "1. Install the `landingai` python package.\n",
-    "2. We prepared a video clip with license plates from different cars on a street. We need to download the video clip to local.\n",
+    "2. We prepared a video clip with license plates from different cars on a street. Download the video clip to your local drive.\n",
     "\n",
     "The video file will be downloaded at `/tmp/license-plates.mov`"
    ]
@@ -163,7 +163,7 @@
     "<a name=\"ocr-and-data-retrieval\"></a>\n",
     "\n",
     "## OCR and Data Retrieval\n",
-    "In this section, we'll pass the cropped license plate images through an Optical Character Recognition (OCR) model. The OCR model's job is to convert the image of the license plate into a string of text, allowing us to retrieve the license plate number."
+    "In this section, we'll pass the cropped license plate images through an optical character recognition (OCR) model. The job of the OCR model is to convert the image of the license plate into a string of text, allowing us to retrieve the license plate number."
    ]
   },
   {
@@ -174,7 +174,7 @@
    "source": [
     "from landingai.predict import OcrPredictor\n",
     "\n",
-    "# NOTE: below key has a rate limit, set to your own API key for production use.\n",
+    "# NOTE: The API key below has a rate limit. Use an API key from your own LandingLens account for production use.\n",
     "API_KEY = \"land_sk_WVYwP00xA3iXely2vuar6YUDZ3MJT9yLX6oW5noUkwICzYLiDV\"\n",
     "ocr_predictor = OcrPredictor(api_key=API_KEY)\n",
     "\n",