Using Google Vision API For OCR and Text Detection

Posted By : Priyansha Singh | 29-Jul-2022


OCR and Text Detection With Google Vision APIs


OCR or Optical Character Recognition has been in the technical landscape for quite a long time. However, with every new innovation and enhancement in related technologies, it has now become much more accessible and easier to use.


In that context, Google Vision APIs have emerged to be an immensely helpful tool for OCR and text detection. It is a cloud OCR service that automatically detects as well as extracts text and data from PDF files and scanned documents. Undoubtedly, it goes beyond simple OCR to also identify the contents of fields in forms and data stored in tables. 


Furthermore, Google Cloud Vision APIs can seemingly give life to limitless application possibilities, combined with Python libraries. It is fundamentally Google’s pre-trained model that detects faces and objects and performs image recognition, labeling, classification, and text extraction of handwritten or printed text from images. Moreover, it enables developers to integrate built-in features with utmost ease and reliability.  


In this blog, we will shed some light on what OCR means, how it works, and how Google Vision API can be effectively used for OCR and text detection. So, let’s get started.

Google Vision API For OCR and Text Detection

Understanding OCR


Optical Character Recognition or OCR is primarily a technique that involves converting digital images of text into machine-readable data. For instance, by recognizing the patterns of light and dark, our eyes can efficaciously read text on a given medium whilst translating those patterns into characters and words, and then subsequently attaching meaning to it. In a similar way, OCR attempts to mimic the way our visual system functions and operates, and their detection mechanisms are fuelled by neural networks.


Essentially, there are two methods to perform OCR:


  • Matrix matching
  • Feature detection 


Matrix matching is simpler and less complex than the other – it takes an image and compares it to existing libraries of character matrices and templates in order to generate a match.  


In reference to that, feature detection is more complicated than it seems for general features such as curvatures, diagonal lines, intersections, and more while comparing it to other features on the image within a certain distance. 


Enter Cloud Vision API


Cloud Vision API allows developers to contemplate the content of any image by entailing robust and powerful machine learning models in simple and easy-to-use REST and RPC APIs. It can swiftly classify images into a plethora of categories, detects individual faces and objects within images, and finds and reads printed characters as well as words contained within images. 


Furthermore, the Cloud Vision API encapsulates a wide selection of image recognition tools that can be incessantly leveraged for building real-world applications such as moderating offensive content and categorizing images. You need to leverage their admin panel to generate a client JSON file that contains all the vital details and information to access the Vision API. From there, you need to set up your development environment correctly configured with the related and required Python packages to interface with the Google Cloud Vision API.


Here are some of the prominent attributes of Google Cloud Vision API:


  • Face detection
  • Image attributes
  • Detect labels
  • Optical Character Recognition (OCR)
  • Web detection
  • Detect multiple objects
  • Detect explicit content (SafeSearch)


How Does It work?


The tool primarily performs a layout analysis on images in order to segment the location of the text. When the general location is successfully detected, the OCR module next performs a text recognition task on the specified location to produce the text. At last, at the post-processing step, all the errors are corrected by feeding them through any predefined language model or dictionary.


All of the above-mentioned processes are carried out by a convolutional neural network in which each neuron is connected separately to a subset of neurons present in each layer. Basically, convolutional neural networks are a subset of neural networks that are architected to imitate the hierarchical structure of our visual cortex that helps in determining how we identify objects.


As we can anticipate, the algorithms powering these models are difficult to understand and highly convoluted. However, luckily, all of this has been seamlessly abstracted away by Google and packaged all together in formats that are convenient and easy to use. 


Also Read: What Is The Future Of Conversational AI In The Metaverse


The Cloud Vision API and OCR


The Cloud Vision API can seamlessly detect as well as extract data from any image. In order to support Optical Character Recognition (OCR), there are two core annotation features available:




This function is used to detect and extract text from any given image. For instance, a photograph might have a traffic sign or a street sign. The JSON inculcates the complete extracted string along with individual words as well as their bounding boxes.




This function is also used to extract text from any image, however, the entire response is well-optimized for dense texts and complex documents. The JSON includes words, blocks, paragraphs, pages, and break information.


Benefits of Using OCR With Google’s Vision API


Here, we have mentioned some of the top features and benefits of leveraging OCR with Cloud Vision API:


  • Detects and extracts relevant and useful textual information from any image – by amalgamating it with Google’s Natural Language API, the program can not only be used for text detection, but also for extracting the required data at scale.
  • Expedites certificate as well as document verification for video KYC – by automating the all-inclusive process of data detection and extraction, certificate and document verification can subsequently be expedited, thereby, saving a lot of effort and time for your business.
  •  Optimized for dense text as well as big documents such as PDFs – the program can function with utmost precision and ease through documents like PDFs that are at scale and text-heavy.
  • Integrates seamlessly with both new and existing applications – the Vision API is so robust and versatile that it can be integrated for a multitude of applications. You can also leverage Cloud Vision APIs for building a new application in order to help streamline as well as innovate, empower, and accelerate your business processes.


Looking For OCR and Text Detection Using Google Vision API?


At Oodles Technologies, we provide AI-powered OCR services, enabling businesses to scan data from physical documents and garner useful insights. As an established AI app development company, we assist organizations of all shapes and sizes to harness the power of OCR applications and augment their business workflows while achieving greater efficiency and productivity. Our AI and ML-driven OCR solutions are effective at capturing critical information, digitizing content, and furnishing actionable insights for smart and better decision-making. Moreover, we program AI OCR solutions with machine learning as well as computer vision algorithms to automate document scanning and processing with optimum accuracy and efficiency. We also provide Google Vision API assistance for building next-gen OCR and text detection applications. If you are interested in accelerating your data extraction processes with AI-powered OCR apps, feel free to get in touch with our team. We will get back to you within 24 hours. 


About Author

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Priyansha Singh

Priyansha is a talented Content Writer with a strong command of her craft. She has honed her skills in SEO content writing, technical writing, and research, making her a versatile writer. She excels in creating high-quality content that is optimized for search engines, ensuring maximum visibility. She is also adept at producing clear and concise technical documentation tailored to various audiences. Her extensive experience across different industries has given her a deep understanding of technical concepts, allowing her to convey complex information in a reader-friendly manner. Her meticulous attention to detail ensures that her content is accurate and free of errors. She has successfully contributed to a wide range of projects, including NitroEX, Precise Lighting, Alneli, Extra Property, Flink, Blue Ribbon Technologies, CJCPA, Script TV, Poly 186, and Do It All Steel. Priyansha's collaborative nature shines through as she works seamlessly with digital marketers and designers, creating engaging and informative content that meets project goals and deadlines.

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