Many companies are usually overburdened with thousands of documents to process, analyze, and transform to carry out their daily operations. They work on documents such as receipts, invoices, statements, forms, contracts, and many other pieces of unstructured data and it’s crucial to be able to understand the information embedded within such unstructured data quickly. The latest advances in computer imagery, it has enabled us to make great progress in easing the burden of document categorizing and understanding. At Janeson, we have a team of experts in python and machine learning who can assist you with extracting texts from your images. Contact us or visit today and help you get what you need.

While humans can understand the content of an image simply by looking, computers don’t work that way. We see the text on the image as text and read it. Computers need something more concrete, organized in a way that they can comprehend. Image processing helps to extract text from an image. This is where the use of Optical Character Recognition (OCR) comes in. OCR has been utilized in the recognition of car plates from a camera or hand-written documents that need to be converted into digital copies. Although it is not always perfect, it’s convenient and makes it easier and faster to do the task. 

For Optical Character Recognition to perform well, it utilizes a popular open-source tool called Tesseract, an OCR engine for various operating systems. You can do optical character recognition in python but you have to install tesseract on your system and then run the code. Tesseract will eventually start the process with the image input as an argument. The function is a result of the program output. The program will output the content on the screen as print. Tesseract provides an easy-to-use interface as well as an accompanying python client library and tends to be a useful tool for OCR projects. 

Uses of OCR

1. Before, digitization of documents was made by manually typing the text in the computer. But through OCR, this process has been made easier as nowadays documents can be scanned, processed and the text extracted and stored in an editable form such as a word document.
2. If your phone has a document scanner, such as Adobe scan, perhaps you have encountered ORC technology in use.
3. OCR can also be used in airports to automate the process of passport recognition and extraction of information from them.
4. Also, ORC is used in the automation of the data entry process, detection, and recognition of car number plates. 

How it works

The first thing is to set up python libraries to use, it’s usually a one-step process. With PyTesseract, you will do the following two steps;

• Install the python library. Simply open your command line and window and type “pip install pytesseract”
• Install the Tesseract application

Once you’ve undertaken the above steps, you’re ready to get started. You can use any image to test the program, but the image should be very clear. It should not have rotation, blur, or a background. Plain black and which are preferred. If your image is not clear, you will have to do some image processing before running tesseract. 

Run the program to view the text. All will be shown in the terminal.

There are other modules used besides pyteserract. They include the following;

• Pyocr. It provides more sensing options such as sentences, digits, or words
• Tesserwrap
• Pytesser

They all use the same OCR engine beneath.

Requirements

• Requires python 2.5 or the latest versions
• Python Imaging Library (PIL)

Lately, more cloud service providers such as including GoogleVision, AWS, Textract, AzureOCR, and DropBox, among others are rolling out text detection capabilities alongside their various computer vision offerings. It is an interesting period in the field, as computer vision techniques are becoming widely available to create many uses. Although there are instances where we might require to call non-traditional OCR where these existing generic solutions are not exactly the right fit. For example, in detecting arbitrary text from images of natural scenes. Problems of this nature are formalized in the COCO-Text Challenge, where the goal is to extract text that might be included in road signs, advertisements, and many others.

The text extraction from images of complex documents is another area that suffers similar challenges. Many types of documents are relatively unstructured in their layouts and have text elements scattered throughout such as invoices, reports, forms, receipts.

Text Extraction from Images Using Machine Learning

It starts with gathering all the related features of a particular image. With the help of machine learning algorithms, text extraction, and enhancements are applied. And finally, the extracted text is collected from the image and transferred to a particular application or a certain file type. There are many types of text extraction algorithms and techniques used for various purposes. They include;

1. Region-Based Method. This method utilizes a sliding window to detect text from any kind of image. This method depends on different factors, such as color, edge, and shape.
2. Texture-Based Method. It uses several kinds of texture and its properties to extract text from an image.
3. Hybrid Technique. It is a combination of regional and texture-based methods. To begin with, the regional-based approach is used to detect text. Later, with the usage of the texture-based method, all the features are extracted from the text region.
4. Edge-based method. This technique is based on the detection of the edges of every letter and digit. This process is used to develop a high-level difference between text and the background.
5. Morphological- Based Method. This technique is used to extract all the text-related features from the processed image.

Do you think that text extraction from images using python in machine learning might be beneficial to your company or speed your work up? Don’t hesitate to contact Janeson. Our team of experts will discuss details with you about implementing text extraction solutions to your business. Janeson is the best place for you to get solutions.