Optical character recognition technology is a system that transforms printed or handwritten images and text into digital form, even if a machine can exploit it. Unlike humans, who can recognize the characters and text quickly, plans aren’t smart enough to perceive the data available in a picture. That’s why much research has been conducted that generates results on converting a document picture to machine-recognizable text.
OCR scanning services are challenging due to the diverse fonts, styles, and languages in which text can be recognized and written. Hence, methods from different disciplines have been introduced under the OCR process to provide accurate results. These methods include pattern classification, image processing, and natural language processing. Moreover, they are enforced to address different issues. This blog will cover all the scan text recognition applications, challenges, and techniques.
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The traditional OCR systems aren’t computers but instruments or devices that recognize characters or text at very low speeds. M. Sheppard, in 1951, designed a robot that is considered the gateway to advanced OCR scanning services. Robots can easily interpret one-by-one musical notations and words on printed paper but only identify 23 characters. Furthermore, the machine also copied a typewritten report.
In 1954, J. Rainbow built a machine that quickly read one per minute of uppercase English characters. The previous OCR scanning services had slow recognition speed and error issues. Over the past few years, significant research has been conducted on OCR, leading to the origination of DIA (Document Image Analysis), omni-fonts, and multilingual handwritten text. Hence, extensive research has been conducted on improving the accuracy of scan text recognition.
There are various directions in which research has been conducted on OCR over the past few years. This part considers different OCR types that come to the fore as an outcome of the investigation. Firms usually categorize these types depending on character connectivity, image acquisition mode, and font restrictions. Depending on the input type, the digital OCR is classified as machine-printed and handwritten recognition.
The previous OCR services are relatively more straightforward, as their characters typically follow uniform directions, and the character position can be quickly predicted. Moreover, these systems can be subdivided into two diverse categories:
Scan text recognition is relatively simple as it performs real-time character recognition, and it is less complicated as it can capture transient and time-based data such as velocity, speed, direction of writing, stroke numbers, etc.
OCR provides a massive number of valuable applications. During its early days, it has been helpful for bank checks, signature verification, and mail sorting. Moreover, firms can utilize it for advanced form processing in areas where data availability is vast and in printed form. Additional forms include an OCR passport scanner, utility bills, automated number plate identification, and pen computing.
Moreover, OCR’s remarkable application assists visually impaired and blind people to read and recognize text.
The OCR procedure consists of diverse phases, such as:
1. Image Acquisition: The scanner and camera are the outside sources that quickly capture the user image.
2. Preprocessing: After acquiring an image, diverse preprocessing methods will be able to enhance the image quality. Usually, the techniques are thresholding, noise removal, extracting images, etc.
3. Character Segmentation: OCR easily separates characters from images so they can be sent to the recognition engine. The simplest methods are interconnected with projection profiles and component analysis. However, a modified OCR scanning service is used to overcome these issues in complicated scenarios where characters are broken, overlapping, or any existing noise.
4. Feature Extraction: The segmented characters are transformed into different features. The characters quickly recognize the text from images due to OCR systems. The fetched characters must minimize intra-class discrepancy, improve inter-class variations, and be effectively computable.
5. Character Classification: This approach reaches the characteristics of the segmented picture into diverse classes and categories. Let’s explore different character classification methods:
6. Post-processing: After classifying, the outcomes must be more accurate and reliable, specifically for complicated languages. These techniques can be used to enhance the reliability of OCR processes. Moreover, they utilized geometric, linguistic context, and natural language processing to rectify errors in OCR outcomes.
An optical character recognition technology comprises different phases involving segmentation, preprocessing, acquisition, classification, feature extraction, and post-processing. Using a combination of these methods, digital OCR is the future of the business industry. Moreover, this system has different use cases, such as libraries and number plate recognition. At last, implementing OCR services helps employees in this digital age.
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