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How to Convert Image to Text Using Python

Leveraging artificial intelligence (AI) and optical character recognition (OCR), it's possible to draw out text from an array of file formats. This extraction can be further simplified with coding. Today, we delve into the method of translating images to textual data using the powerful Python programming language.

Organizations in the modern era are bombarded with a significant amount of unstructured data in a myriad of formats – PDFs, scanned files, images, and the like. Manual extraction of crucial textual information from these heaps of data is a taxing task bound to result in errors and inefficiencies.

However, there's a silver lining to this – thanks to strides made in AI, it's now possible to streamline this task with code. Throw AI-fueled OCR algorithms into the mix and one can efficiently and accurately translate image-based text into accessible, actionable and searchable data.

This piece focuses on various types of images and the corresponding methods required to extract text from them. We highlight the limitations of some common approaches and offer practical solutions to enhance output. So, why is it necessary to translate images to text?

Why is Text Extraction Important?

Numerous entities churn out image data from operational documentation. Sadly, this text encounters issues when it comes to viewing, editing, or analysing it since it's not searchable. Hence, it becomes imperative to extract or translate it into string data to capture and utilize it.

In the scenario of extracting invoice details, dates, supplier information, amounts, and other textual information from invoice images- one can store such data for auditing, tax purposes or to assess supplier performance.

The necessity for extracting text isn't just restricted to invoices. Other important use cases include the digital conversion of recruitment forms, resumes, healthcare records, food labels, ID document scans, and location-specific images such as store names and street signs.

What Kinds of Images are Suitable for Text Extraction?

In Python, text extraction lends itself to all types of images theoretically speaking. However, depending on expected outputs, the complexity of code and accuracy may greatly differ.

Images with a simple setup, sporting large text, limited words, simplistic fonts, and clear contrast between text and images, may only require a few lines of code. More complex images showcasing different fonts, noisy backgrounds, shadowed or skewed text or handwritten text will likely prove more challenging. Such images fight for extra coding efforts within a DIY coding program. They demand preliminary processing of text prior to extraction and further editing thereafter, to rectify text post-extraction.

Translating Simple Images to Textual Data in Python

For straightforward images, the ensuing methods are ideal.

Tesseract and OpenCV

Tesseract is a revered, open source OCR engine that assures accurate text extraction from images. Its counterpart, the Open Source Computer Vision Library (OpenCV), is a software library rooted in machine learning that offers a variety of options and algorithms to work with videos and images.

Pairing Tesseract and OpenCV, users can extract data from images using Python. After Tesseract is installed on the system, the pytesseract library, a Python wrapper along with OpenCV, should be installed. This is followed by simple steps to translate the text image into a string using Tesseract.

Another alternative in converting images to text is the online service OnlineOCR.


Fairly efficient and user-friendly, easyOCR is a Python library that showcases a simple interface to extract text from basic images. A brief command initializes text extraction. The readtext method then returns a list of text detection results, easch containing extracted text, bounding box coordinates, and a reliability score. Handling these results is made easy with features allowing for text manipulation or printing.

Other Python Libraries

Besides pytesseract and easyOCR, there are other Python libraries at our disposal that come with OCR capabilities to mine text from images. They provide a cohesive interface to use these engines for text extraction. Variations like PyOCR, OCRopus, provide supplementary choices and flexibility in relation to OCR in Python. Some libraries can even be used for both single-page and multi-page document OCR.

Limitations of Python Libraries

While they work wonders on basic images, open-source Python libraries may encounter shortfalls when complex images come into play. They produce inaccurate results if the background is pixelated, blurry or matches the text color, or if dealing with an image is a handwritten or scanned copy. They perform poorly if the image accommodates multiple columns or irregular text placement. Also, they are not equipped with natural language processing (NLP) features to check and improve output. If the input deviates from standard, the Python libraries output incorrect results.

Improving Python Libraries' Efficiency

The efficiency of Python libraries can be optimized by converting images. Preliminary to text extraction, the image must be converted to grayscale or black and white. Following this, grayscale can be evolved into a binary format where text is shown as black pixels against a white background.

To augment efficiencies, additional code for image preprocessing can be written. Common preprocessing tasks encompass applying filters to enhance clarity, adjusting text and background contrast, correcting image skew or rotation, normalizing varying text size, and more.

Cloud APIs

Cloud providers offer completely managed OCR services for text extraction from images. They grapple with the underlying complications of text extraction so users get string output simply by inputting an image to an API. Topnotch cloud OCR services include Microsoft Azure Cognitive Services, Amazon Textract, and Google Cloud Vision API.

In essence, the conversion of images to text considerably enhances the accessibility and productivity of any data-heavy business operation. Benefiting from the power of Python libraries to streamline this process further leverages the overall efficiency and accuracy of text extraction. Even better, with the advent of AI and OCR technologies, the process is only poised to get more streamlined and refined in the future.