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Text Extractor: Why and How to Extract Text from Images and Video

A text extractor is a software tool that identifies and copies text from various file types, images, and videos by using optical character recognition (OCR) technology. By automating this process, text extractors save time and effort for web developers and designers while ensuring accuracy.

There are several types of text extraction tools:

  • Image-based. These tools specialize in extracting text from image files like JPGs, PNGs, or GIFs. They can recognize printed or handwritten text within the image file.
  • Video-based. Video extraction tools analyze video frames to detect embedded textual information, such as subtitles or annotations.
  • Mixed-media. Some advanced text extraction tools can handle both images and videos for comprehensive analysis.

OCR technology is the core component behind most modern-day text extractors. It enables computers to recognize characters within an image or video frame by analyzing their shapes and patterns. The extracted data can then be converted into machine-readable formats, such as plain-text documents (.txt), Word documents (.docx), PDFs (.pdf), etc., making it easier for users to search, edit, store, or share the content across platforms.

This is part of a series of articles about image editing.

Manual data entry is time-consuming and prone to errors. With text extraction tools, organizations can automate the process by pulling relevant information from scanned documents or images directly into databases or spreadsheets. These tools not only save time but also reduce human error in data management tasks.

Text extraction from visuals enables developers to incorporate image-based material as part of their search engine optimization (SEO) tactics via alt tags or labels, while enhancing website availability for people with vision impairments. Extracting text from images allows web developers to include image-based content in textual format, through alt tags or captions, while improving overall website accessibility for users with visual impairments.

  • Meme analysis. Memes are popular internet phenomena that often contain embedded text within images. By using an online text extractor to analyze meme contents, marketers can better understand trends and track brand sentiment on social media platforms.
  • User-generated content moderation. Social media sites must filter out inappropriate content such as hate speech or explicit language found in user-generated photos or videos. Text extraction helps identify these violations and remove them promptly before causing harm to other users.

Text extraction can facilitate the process of collecting data from a variety of sources for students, educators, and researchers. For example, extracting text from historical documents or academic papers allows users to compile data into a single document for easy analysis and reference.

Videos are an increasingly popular medium for content consumption. However, not all videos come with subtitles or transcriptions. Text extractors can automatically generate transcripts or captions from video files, making them more accessible to a wider audience while improving user experience.

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Read our guide to overlay text on image.

OCR is a widely used method to extract text from images. It involves analyzing an image’s pixels to recognize characters and convert them into digital text format. Modern OCR engines use machine learning algorithms to improve their accuracy in recognizing various fonts and languages.

An example of an open-source OCR engine is Tesseract. Developed by Google, Tesseract can be integrated into web applications using libraries like pytesseract for Python or node-tesseract for JavaScript.

In addition to images, extracting text from videos requires additional steps due to motion and varying frames per second (FPS). The following are some common techniques:

  1. Frame selection. Extracting keyframes at regular intervals helps reduce computational load while ensuring accurate results.
  2. Motion detection. Identifying regions with significant movement allows focus on relevant areas within each frame where textual information might appear.
  3. Binarization and noise reduction. Converting video frames into binary format simplifies character recognition while reducing noise enhances overall accuracy.
  4. OCR. Finally, OCR engines are used to extract text from the processed frames.

In addition to basic character recognition capabilities offered by standard OCR engines, some cutting-edge solutions provide additional features tailored specifically for web developers and designers working with rich media assets:

  • Language support. Advanced text extractors can recognize and process multiple languages, including non-Latin scripts like Chinese or Arabic.
  • Layout analysis. These tools can identify the structure and layout of the extracted text, preserving its original formatting when converting to other formats.
  • Data extraction. Some solutions offer specialized data extraction features for specific use cases, such as extracting tabular information from scanned documents or pulling metadata from images and videos.

While you can implement your own solution using open-source tools, there are several cloud-based APIs and services available for text extraction. These solutions provide pre-prepared models, which can accommodate a wide variety of needs with minimal setup. Some popular options include:

  • Cloudinary OCR and Text Extraction add-on. Cloudinary is a cloud-based, end-to-end image and video management solution. The OCR Text Detection and Extraction add-on, powered by the Google Vision API, lets you automatically extract all detected text from files uploaded to Cloudinary by your users, including images and multi-page documents like TIFFs and PDFs.
  • Microsoft Azure Computer Vision API. This API provides OCR functionality in addition to analyzing images for content moderation purposes.
  • Amazon Textract. This dedicated service by AWS is specifically designed for extracting text from documents, forms, and tables within images or PDFs.

A good text extraction tool should easily integrate into your existing workflows and systems. Look for tools with well-documented APIs or SDKs that support popular programming languages like Python, Node.js, or Ruby on Rails. Additionally, check if the tool offers prebuilt plugins or extensions for commonly used content management systems (CMS) such as WordPress or Drupal.

The accuracy of extracted text is crucial when dealing with important data from images and videos. Ensure that the chosen tool has a high success rate in recognizing various fonts, sizes, colors, and orientations. It’s also essential to assess its efficiency in terms of the rate at which it can handle a substantial number of media files.

A robust text extraction tool should have support for at least these popular formats:

  • JPG. A common image format widely used across websites due to its compression capabilities.
  • PNG. An image format known for lossless compression, which preserves quality better than JPG but results in larger file sizes.
  • GIF. A popular choice for simple animations due to its small file size. However, it isn’t suitable for complex images because it only supports up to 256 colors.
  • MP4. A widely used video format that offers high-quality compression and compatibility with most devices and platforms.

Your text extraction needs may grow over time, so it’s essential to choose a tool that can scale along with your project. Consider whether the solution supports batch processing for bulk operations or if it has built-in features to handle increased workloads efficiently. 

Cloud-based solutions like Cloudinary are often more scalable than on-premise options due to their flexible infrastructure and ability to allocate resources as needed.

We’ll discuss some best practices for extracting text from images and videos. For optimal performance, select high-quality source files.

Selecting high-quality source files is essential to accurate text extraction. Make sure to use high-resolution images or videos with clear, legible text, which will make it easier for the text extractor to accurately recognize characters.

If possible, clean up your images before processing them through a text extraction tool. Doing so may involve removing unnecessary elements such as watermarks or background noise that could interfere with character recognition. 

You can use image editing software or services like Cloudinary’s Image Transformation API to enhance image quality before extracting its content.

  • Avoid using light fonts on pale backgrounds or dark typefaces on dusky backgrounds.
  • Choose font colors that contrast well with their background colors so that they are easily distinguishable by OCR engines.
  • If necessary, adjust brightness levels within an image editor prior to running it through a text extractor tool.
  1. Avoid using overly stylized fonts as they might be difficult for OCR engines to read correctly.
  2. Maintain a minimum font size of 10 points to ensure legibility and accurate character recognition.
  3. Consider using common fonts like Arial, Times New Roman, or Helvetica for better OCR results.

To make the most out of your extracted data, it’s crucial to maintain proper documentation and stay organized throughout the process. Keep track of the source files used in each extraction task along with any preprocessing steps taken prior to running them through a text extractor tool.

Cloudinary is a cloud-based service that provides an end-to-end image and video management solution, including uploads, storage, transformations, optimizations, and delivery. It offers a rich set of image transformation capabilities such as cropping, overlays, and graphic improvements.

The OCR Text Detection and Extraction add-on, powered by the Google Vision API, integrates seamlessly with Cloudinary’s upload and transformation functionality. It extracts all detected text from images, including multi-page documents like TIFFs and PDFs.

You can use the extracted text directly for a variety of purposes, such as organizing or tagging images. Additionally, you can take advantage of special OCR-based transformations, such as blurring, pixelating, or overlaying other images on all detected text with simple transformation parameters. You can also use the add-on to ensure that important texts aren’t cut off when you crop your images.

Use the add-on in normal mode to capture text elements within a photograph or other graphical image, or use it in document mode to capture dense text when scanning a document. If the image includes non-Latin characters, you can instruct the add-on to analyze the image for a specific language.

Sign up for free and try Cloudinary for text extraction today!

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