OCR stands for Optical Character Recognition, a technology that enables computers to recognize text from images, such as scanned documents or PDFs. OCR software works by analysing the shapes and patterns of the characters in an image and then converting them into editable text that can be used in a variety of applications. For example, OCR is commonly used for data extraction, allowing users to extract text from documents and images and convert it into a machine-readable format. This can be useful for various applications, such as digitizing paper documents, extracting data from forms, and extracting text from scanned PDFs or images. Learn about how the AI-powered OCR software enables document processing.
Using OCR to extract data from PDF Documents.
There are several ways to extract data from PDF documents. However, it is essential to note that the accuracy of the extracted data depends on the quality of the original PDF document and the method used for extraction.
Extracting data from PDF documents using only OCR can be challenging due to several reasons, including:
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Poor Quality of pdf: The quality of the PDF may not be suitable for OCR, whether the documents are skewed because of improper scanning or are of low resolution, extraction of text from such documents using plain OCR lead to errors and inaccuracies in the extracted data.
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Complex Document Layouts: PDFs can have complex layouts, with tables comprising of 100s of line items, tables spanning pages, pdfs containing multiple documents. which can make it all the more difficult for OCR software to recognize the text accurately.
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Handwritten text: It can be challenging for OCR to extract from handwritten documents or documents that have both printed and handwritten text.
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Using OCR to extract data from images.
Processing images through OCR involves using software that can recognize text in an image and convert it into an editable format. Here are the steps usually involved to process an image through an OCR -
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Pre-process the Image: Before running OCR on the image, it may be necessary to pre-process it to improve OCR accuracy. This may include adjusting the brightness and contrast of the image, removing noise, and improving the resolution.
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Run OCR: Once the image has been pre-processed, the OCR software can be used to recognize the text in the image. Most OCR software will have an option to select the language of the text to be recognized and may also have settings for the level of OCR accuracy.
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Review and Correct: After the OCR software has completed the recognition process, it is important to review the extracted text to ensure accuracy. Depending on the quality of the original image, OCR errors may occur, so it is important to correct any errors manually.
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Export the Extracted Data: Once the extracted text has been reviewed and corrected, it can be exported to a variety of formats, such as plain text, Microsoft Excel, or a database.
Extracting data from Images using only OCR can be challenging due to several reasons, including:
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Image Quality: The quality of the image can greatly affect the accuracy of the OCR process. If the image is blurry, has low resolution, or contains noise, the OCR software may not be able to recognize the characters accurately.
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Text Orientation: If the text in the image is not oriented in a standard direction, such as if it is rotated or skewed, the OCR software may not be able to recognize the characters correctly.
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Complex Background: If the image has a complex background or contains images, graphics, or logos, it can be challenging for the OCR software to distinguish between the text and the background, which can lead to errors in the extracted data.
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Handwritten Text: OCR software is designed to recognize printed text, but it may not be able to recognize handwritten text accurately. If the image contains handwritten notes or annotations, the OCR software may not be able to extract the data correctly.
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Language Support: OCR software may not support all languages, which can limit the accuracy of the extracted data. If the image contains text in a language that the OCR software does not support, the extracted data may be inaccurate or incomplete.
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Using OCR to extract data from tables.
OCR can be used to extract data from tables in images or PDFs. However, extracting data from tables using OCR can be more complex than simply recognizing text in a paragraph. Here are some steps that can be taken to extract data from tables using OCR:
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Identify the Table: First, the table in the image or PDF must be identified. This can be done manually by selecting the table or by using software that can automatically detect tables.
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Pre-process the Table: Before running OCR on the table, it may be necessary to pre-process it to improve OCR accuracy. This may include adjusting the brightness and contrast of the table, removing noise, and improving the resolution.
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Extracting text from the Table: Once the table has been pre-processed, OCR software can be used to recognize the text in the table. However, OCR software may not recognize the table structure correctly, resulting in incorrect data extraction.
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Post-Processing: After the OCR software has completed the recognition process, the extracted data may need to be post-processed to correct any errors and ensure accuracy. This may include removing unnecessary characters, correcting misrecognized characters, and formatting the data.
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Export the Extracted Data: Once the extracted data has been reviewed and corrected, it can be exported to a variety of formats, such as plain text, Microsoft Excel, or a database.
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Extracting data from tables using only OCR can be challenging due to several reasons, including:
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Table Structure: The structure of the table can make it difficult for OCR software to accurately recognize the text. For example, if the table has merged cells or irregular borders, the OCR software may not be able to distinguish between different cells or columns, leading to inaccuracies in the extracted data.
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Text Size and Font: The size and font of the text in the table can affect the accuracy of the OCR process. If the text is too small or has a non-standard font, the OCR software may not be able to recognize the characters accurately.
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Table Content: The content of the table can also affect the accuracy of the OCR process. If the table contains special characters or symbols, or if the table has a mix of text and numbers, it can be challenging for the OCR software to accurately recognize and extract the data.
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Background Noise: The presence of background noise, such as lines or grid marks, can make it difficult for OCR software to accurately recognize the text in the table, which can lead to errors in the extracted data.
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Language Support: OCR software may not support all languages, which can limit the accuracy of the extracted data. If the table contains text in a language that the OCR software does not support, the extracted data may be inaccurate or incomplete.
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How an AI-powered OCR product like Kanverse is different from traditional OCR.
Optical Character Recognition (OCR) technology is used to convert images of text into machine-readable text. OCR has been around for many years and has traditionally relied on rule-based algorithms and template matching techniques to recognize and extract characters from an image.
AI-powered OCR, on the other hand, uses AI technologies like Computer Vision in conjunction with OCR to recognize and extract text. Multiple other AI technologies like Fuzzy logic, Natural Language Processing, and Machine learning is used to clean the data and learn about semantics of the text and identify patterns and anomalies. Here are some key differences between AI-powered OCR and traditional OCR:
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Accuracy: AI-powered OCR has been shown to achieve higher accuracy rates of up to up to 99.5% as compared to plain OCR. This eliminates the need for manual touch in most cases leading to touchless automation.
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Flexibility: AI-powered OCR can be more flexible than traditional OCR because it can continuously learn from and adapt to new data and improve its accuracy over time. Traditional plain OCR systems are typically fixed and cannot easily be adapted to new fonts, styles, or languages.
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Complex document handling: AI powered OCR can be used for documents with varied ever-changing formats, complex documents that may be skewed or rotated and with low resolution. It can also be used for documents with tables with 100s of line items and those that span pages.
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Automation: AI-powered OCR can be fully automated, meaning that it can recognize and extract text from images without any human intervention. This can be particularly useful in high-volume applications where large volumes of text need to be processed quickly and without human intervention.
Overall, AI-powered OCR is a more advanced and flexible technology than traditional OCR, offering higher accuracy rates, faster processing times, and greater automation capabilities.
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Benefits of using AI-powered OCR to extract data from documents.
There are many benefits to using AI-powered Optical Character Recognition (OCR) to extract data from documents. Here are some of the key benefits:
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Operations Cost savings: By automating the data extraction process, organizations can save time and reduce costs associated with manual data entry.
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Improved efficiency: AI-powered OCR can significantly speed up the data extraction process, allowing organizations to process large volumes of documents quickly and accurately. Humans are involved only for oversight and not in every transaction.
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Enhanced data quality: AI-powered OCR can help ensure data quality by identifying and correcting errors in data extraction, reducing the risk of human error.
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Improved compliance: AI-powered OCR can help organizations meet compliance requirements by ensuring that data is accurately extracted and properly documented.
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Better insights: AI-powered OCR can help organizations extract insights from unstructured data, such as customer feedback or social media comments, which can be used to inform business decisions.
Overall, AI-powered OCR offers many benefits to organizations looking to extract data from documents. It can improve accuracy, efficiency, data quality, compliance, and insights, while reducing costs and saving time.
About Kanverse.ai
Kanverse brings you the best-in-class IDP software to provide a “Zero-touch” experience. It automates ingestion, classification, extraction, validation to filing of structured, semi-structured and unstructured documents. Extract data from a wide gamut of documents with up to 99.5% accuracy using its multi-stage AI engine. Say goodbye to manual entry, reduce cycle time to seconds, optimize cost by up to 80%, minimize human error, and turbocharge productivity of your team. AP automation software like Kanverse APIA (AP Invoice Automation) is built to do the heavy lifting across your AP cost centers while your staff can focus on productive and business-critical activities. Kanverse can also automate insurance submission workflows, seamlessly processes ACORD and supplemental forms, handwritten documents, and KYC and KYB documents.
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About the Author
Aritro Chatterjee, Product Marketing Manager, Kanverse.ai