What is optical character recognition?
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Optical Character Recognition (OCR) is a technology used to convert images of handwritten, typewritten, or printed text into machine-readable formats. OCR plays a crucial role in various applications such as improving archival descriptions, enhancing document searchability, and enabling automatic text identification in images. Different approaches like customized preprocessors, deep learning models, and image segmentation techniques are employed to enhance OCR accuracy and performance. By utilizing OCR, tasks like image to text conversion, license plate recognition, Sudoku solving, and handwriting recognition can be efficiently accomplished. Overall, OCR is a valuable tool that bridges the gap between physical documents and digital data, enabling efficient data processing and analysis across diverse domains.
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| Papers (5) | Insight |
|---|---|
Open access•Journal Article | Optical Character Recognition (OCR) is an image processing technique for automatic text identification. The paper discusses OCR's accuracy impacted by segmentation quality using active contour algorithms. |
13 Nov 2014 7 Citations | Optical Character Recognition (OCR) is the process of converting images of text into machine-readable format for editing, indexing, and storage reduction purposes, as discussed in the paper. |
05 Sep 2021 | Optical Character Recognition (OCR) is a pattern recognition tool. The paper introduces a novel method to enhance OCR accuracy by customizing preprocessing based on specific OCR engine gradients. |
| Optical Character Recognition (OCR) is a technology that recognizes text in images and converts it into machine-encoded text, aiding in the analysis of typewritten cultural documents. | |
01 Sep 2019 4 Citations | Optical Character Recognition (OCR) is the process of converting images of text into machine-encoded text, crucial for reading damaged faxes and PDFs, as discussed in the paper. |
Related Questions
Where do optical character recognition used?4 answersOptical Character Recognition (OCR) is used in various fields such as business process activities, number plate recognition, KYC and banking processes. It is mainly used for converting printed, handwritten, or transcribed text into machine-encoded text that can be edited and reused. OCR technology finds applications in industries where there is a need for digital conversion of printed and textual media, such as prescriptions, invoices, receipts, etc.. It is also used for data entry from printed paper records, digitizing printed texts for electronic editing, searching, and storage. OCR is a crucial technology used in many industries to shorten turnaround times while increasing accuracy. It is employed in post-processing techniques such as error correction algorithms to detect and correct errors in OCR output. Additionally, deep learning techniques like Conventional Neural Networks have been shown to improve the accuracy of OCR systems.
How to recognize character using image and text?3 answersCharacter recognition using image and text can be achieved through various techniques. One approach is to use an encoder-decoder architecture, where text images are converted into representative features and then decoded into a sequence of characters. Another method involves employing an image-to-character (I2C) mapping to detect character candidates from images based on different alignments of visual features, followed by character-to-word (C2W) mapping to recognize the scene text by decoding words from the detected character candidates. Additionally, self-supervised learning can be utilized to explore robust text feature representations on unlabeled real images, enabling versatile augmentations for general text representation learning. Furthermore, a character recognition method can involve determining the position of a text box in an image, intercepting the target image corresponding to the text box, and using feature description operators and classifiers to recognize the character. Another approach involves classifying text areas in an image and then performing character recognition on the classified character regions to improve accuracy.
Does gender affect the accuracy of optical character recognition?5 answersGender does affect the accuracy of face recognition. Multiple studies have shown that recognition accuracy is lower for women compared to men. This is due to a combination of factors, including the impostor distribution for women having a skew toward higher similarity scores and the genuine distribution for women having a skew toward lower similarity scores. This accuracy difference persists across different datasets and even for image subsets rated as neutral expression or close to zero pitch angle. The accuracy difference also remains when images with forehead obstruction or facial cosmetics are removed. Furthermore, even when deep learning techniques are trained on a balanced dataset of male and female faces, the accuracy difference still exists. Therefore, it can be concluded that gender does have an impact on the accuracy of face recognition.
How can optical character recognition be improved for use with gender-diverse datasets?5 answersOptical character recognition (OCR) can be improved for use with gender-diverse datasets by implementing feature-map alignment and deep learning techniques. Feature-map alignment, achieved through the use of a spatial transformer network, helps align the feature maps of a convolutional neural network model, resulting in improved recognition accuracy for non-centered and non-uniformly scaled characters. Deep learning networks, such as transfer learning with the Inception V3 model, can also be utilized to train and perform OCR. This approach has shown better accuracy than conventional methods and has the potential to overcome recognition issues with poor quality text characters. By combining domain-specific approaches like scale-invariant feature transform (SIFT) and trainable features like combination of shifted filter responses (COSFIRE), gender recognition from facial images can be improved, even in varying poses, expressions, and illumination conditions.
What is optical character recognition?4 answersOptical character recognition (OCR) is the process of converting images of typed, handwritten, or printed text into machine-encoded text. It allows for the digitization of printed texts, enabling electronic editing, searching, and storage. OCR has been widely studied and developed for over 50 years, with applications in various fields such as data entry, document digitization, and machine processes like cognitive computing and text mining. OCR systems can be based on different techniques, including Convolutional Neural Networks (CNNs), which have shown high efficiency in recognizing handwritten data. The accuracy of OCR can be tested and improved by evaluating its performance under different types of attacks. Feature extraction, dataset size, classification techniques, and recognition accuracy are important factors in the study of OCR for specific languages, such as Odia. Additionally, there are methods and apparatus that assist in verifying OCR results, enhancing accuracy and efficiency. Various techniques and methodologies, including deep learning, have been explored in OCR research.
How can we improve the accuracy of optical character recognition (OCR) systems?5 answersTo improve the accuracy of optical character recognition (OCR) systems, several methods have been employed. One approach is the use of post-processing techniques such as error correction algorithms to detect and correct errors in the OCR output. Another method is the utilization of deep learning techniques, such as Convolutional Neural Networks (CNNs), which have been shown to enhance the accuracy of OCR systems. Additionally, techniques involving the assessment and improvement of document quality have been proposed, where a trained model determines adjustments to parameters in an image to prepare it for OCR. Furthermore, the use of principal component analysis (PCA) has been suggested to extract the principal characteristics and features of damaged or unreadable characters, improving OCR detectability. By employing these methods, OCR systems can achieve higher accuracy in character recognition.