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In today’s digital age, the ability to process large amounts of data quickly and accurately is essential for businesses to stay competitive. However, many businesses still rely heavily on paper-based records, forms, and documents, which can be time-consuming and error-prone. This is where OCR (Optical Character Recognition) technology comes in. OCR technology helps businesses convert paper-based records into digital form, making it easier to manage data. It’s particularly beneficial for businesses dealing with large volumes of records, like those in the legal, healthcare, and finance industries.
In this blog, I will discuss improving accuracy by adjusting the PSM (Page Segmentation Mode) in Tesseract, an Open-Source OCR Engine developed by Google.
Tesseract vs Pytesseract
Tesseract and Pytesseract are two OCR engines based on the same underlying Tesseract OCR engine. Tesseract is a command-line tool that can be used in any programming language or platform, while Pytesseract is a Python wrapper specifically designed for use in Python.
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Different PSM Mode
If you go to the official documentation, Tesseract expects a page of text when it segments an input image. If you use OCR on a single character, word, line, or paragraph, use a different PSM (Page Segmentation Mode). You can learn about complete PSM modes using the tesseract –h command.
There are 14 PSM modes as of version 3.21
PSM Mode 0: Orientation and script detection (OSD) only
The –psm 0 mode does not perform character recognition but rather orientation and script detection. This means that it analyzes the input image to determine how the page is oriented and the confidence of the script, but it does not output the recognized text.
OSD returns two main things,
Script: Based on the language detected, what script is it like (Latin, Han, etc.).
Page Orientation in Degrees of angle (0, 90, 180, 270)
Input Image 2:
Input Image 3:
PSM Mode 1: Automatic page segmentation with OSD
PSM mode 1 captures the OSD information and does the Page Segmentation based on it, but the end user won’t know the OSD information but only the OCR results.
Input Image 1:
PSM Mode 2: Automatic page segmentation, but no OSD or OCR
As of today, it is not yet implemented by Tesseract, you can check by running this command: tesseract –help-psm
PSM Mode 3: Fully automatic page segmentation, but no OSD (Default)
The default PSM, known as PSM 3, automatically segments the input text into multiple words, lines, and paragraphs, treating it as a proper page.
Note: There is no OSD performed in PSM 3.
Input Image 1:
PSM Mode 4: Assume a single column of text of variable sizes
PSM 4 will be very useful when dealing with columnar data like tabular data, receipts, tax invoices, spreadsheet data, etc.
PSM Mode 5: Assume a single uniform block of vertically aligned text
This mode assumes the input image consists of vertically aligned and extracts the text from it in a readable horizontal way.
We have seen different PSM modes of PSM 0 to PSM 5 of Tesseract, which can improve accuracy when doing OCR. Different Page Segmentation Modes (PSM) in Tesseract are designed to handle different types of input images and text arrangements. Each mode has specific use cases, and using the appropriate mode for a given image can result in better accuracy and recognition rates. In Part 2 of the blog, we will explore and deep dive into PSM modes 6 to 13.
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Drop a query if you have any questions regarding Tesseract, I will get back to you quickly.
1. What is OCR?
ANS: – OCR stands for Optical Character Recognition, the technology that allows a computer to recognize and convert printed or handwritten text into machine-encoded text that can be edited, searched, and analyzed.
2. What is Tesseract?
ANS: – Tesseract is a free and open-source optical character recognition (OCR) engine developed by Google.
3. What preprocessing steps can I take to improve Tesseract accuracy?
ANS: – Preprocessing steps to improve Tesseract accuracy include thresholding, binarization, deskewing, and denoising the image.
WRITTEN BY Ganesh Raj
Ganesh Raj V works as a Sr. Research Associate at CloudThat. He is a highly analytical, creative, and passionate individual experienced in Data Science, Machine Learning algorithms, and Cloud Computing. In a quest to learn and work with recent technologies, he strives hard to stay updated on advanced technologies along efficiently solving problems analytically.