OCR solutions have played a critical role in the business world with their ability to recognize text from scanned images or documents. Understanding how they work and what are the different components of OCR available will help choose the most suitable for your organization.
What are the components of OCR?
The key components of OCR are:
- Image preprocessing
- Text detection
- Character segmentation
- Feature extraction
- Character recognition
- Handwriting recognition
- Post-processing
1- Image preprocessing
Image preprocessing is the first building block of optical character recognition and involves making sure the quality of the scanned image of the document is the top before the text recognition process begins.
The main objective is to this phase is to improve the image quality to make it easier for OCR to have a very high accuracy of transformation and includes tasks such as removing unwanted noise or artifacts, enhancing the contrast of the text characters.
There are several techniques to be used to improve the quality of the scanned image including
- Noise reduction: This technique is responsible for removing unwanted elements that may appear in the image such as dots and lines.
- Thresholding: This technique is used to convert grayscale image to binary image by separating the text from background.
- Contrast adjustment: This technique is used to enhance the difference between foreground and background.
Before processing images into readable documents in batches, it is crucial to test the quality of a sample image to ensure optimal results. A senior manager from a Saudi Arabian company contacted me once to check their options after scanning over 15,000 documents, because they were not satisfied with the outcome as they did not test the quality beforehand.
To avoid such a situation and the need to redo the work, it is essential to test the quality of sample images beforehand.
2- Text detection
Text detection is in the second list of components of OCR because it identifies the regions within an image that contain text, distinguishing them from non-text areas like graphics or backgrounds. This step is essential in ensuring that the focus only on the relevant parts of the image for character recognition.
Text detection typically involves techniques such as edge detection, connected component analysis, and machine learning models that can locate and segment text areas even in complex layouts. For example, in a scanned document containing both images and text, it must first detect where the text is to process it accurately.
Effective text detection enhances the efficiency and accuracy of the OCR process by ensuring that only the intended text is analyzed, improving recognition rates and reducing errors caused by irrelevant content in the image.
3- Character segmentation
Character segmentation involves separating individual characters from a block of text in an image. This step is critical for accurate character recognition, as it helps with the identification where one-character ends and another begins, especially in cases where characters may be touching or overlapping.
Segmentation techniques often rely on methods such as line detection, word separation, and cutting-edge algorithms that can differentiate characters even in complex fonts or handwriting. For example, in a densely packed or cursive script, character segmentation ensures that each letter is correctly isolated before the recognition phase.
Without precise segmentation, it may struggle to recognize words correctly, leading to errors in the final output. By properly segmenting characters, we can achieve higher accuracy and effectively convert even challenging text layouts into editable and searchable digital content.
4- Feature extraction
Feature extraction involves identifying and extracting key characteristics of each character to enable accurate recognition. During this phase, it analyzes the segmented characters and captures distinct features like lines, curves, corners, and pixel patterns that differentiate one character from another.
These features are then used to match the characters with corresponding templates or learned patterns in the OCR model. For example, it might use the height, width, and shape of a character’s strokes to distinguish between similar-looking letters, such as “O” and “Q” or “1” and “I”.
Effective feature extraction ensures that even characters in different fonts, sizes, or styles are correctly identified. By focusing on the most defining attributes of each character, this process enhances the ability to accurately recognize and convert text, even from challenging or low-quality images.
5- Character recognition
Once the image has undergone preprocessing, text detection, and segmentation, character recognition is the step where the system analyzes each segmented character, compares it to a database of known patterns or models, and assigns the correct digital equivalent. This process can handle both printed and handwritten text.
One of the main advantages of OCR, enabled by accurate character recognition, is its ability to convert physical documents into editable, searchable digital formats. This greatly enhances efficiency in document management, allowing for quicker access to information, reduced manual data entry, and improved storage solutions. OCR components also supports broader accessibility by enabling screen readers to interpret text for visually impaired users, and it facilitates automation in tasks like data extraction and archiving.
6- Handwriting recognition
Handwriting recognition is a specialized process that involves identifying and interpreting handwritten text in images or digital formats. This task is particularly challenging because handwriting varies widely between individuals in terms of style, size, and slant. The process typically involves analyzing the unique strokes, curves, and patterns that form each character or word, and matching them to known templates or models of handwritten characters.
Advanced techniques like machine learning are often employed to improve accuracy, allowing to learn and adapt to different handwriting styles over time. Handwriting recognition is crucial for converting handwritten notes, forms, or historical documents into digital, searchable text, making it useful for a wide range of applications, from digitizing archives to processing handwritten forms efficiently.
7- Post-processing
Post-processing involves refining the recognized text after the initial character recognition step to enhance accuracy and correct errors. This stage typically includes spell-checking, grammar correction, and context-based adjustments to ensure the converted text makes sense.
For example, common errors like misreading “1” as “I” or “0” as “O” can be automatically corrected by comparing the recognized text to a dictionary or language model. Post-processing is especially important in cases where the OCR has to deal with noisy images, complex fonts, or handwritten text, as these factors often lead to recognition mistakes that need to be addressed.
OCR has some notable disadvantages and one of them is accuracy, especially when dealing with low-quality images, poor lighting, or unusual fonts, which can result in incorrect text conversion. Additionally, OCR struggles with highly stylized or cursive handwriting, often requiring significant manual corrections.