PackageX OCR Scanning
PackageX OCR API turns any smartphone into an incredibly powerful universal label scanner. It can read every bit of text, including barcodes, QR codes and other information on the label.
Our OCR technology is the best in the industry. It uses proprietary algorithms and deep learning models to extract information from labels.
Our OCR API has been trained using information from more than 10 million labels. This allows for the highest scanning accuracy in the market, at over 95%.
Our technology can scan in low-light conditions and read labels from any angle.
Create your own OCR scanner app to eliminate pen-and-paper inefficiencies.
Our OCR scanner allows you to extract information from printed text or handwritten labels.
Our OCR software is trained using multilingual label data extracted in over 40 countries.
Detect and extract information from barcodes or QR codes.
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Apryse PDF SDK
Apryse (formerly PDFTron) makes documents work harder for you.
We give organizations the power to handle the full document lifecycle — from secure server-side processing to smooth web-based collaboration — without relying on third-party services.
With Apryse, you can:
Integrate advanced document capabilities like viewing, editing, annotation, and e-signature directly into your applications.
Deploy on your own infrastructure for maximum control, privacy, and compliance.
Scale effortlessly with technology built for high-volume, enterprise-grade workflows.
Deliver modern web experiences that are fast, accessible, and reliable across browsers and devices.
Trusted worldwide, Apryse helps enterprises, developers, and small businesses simplify workflows, cut costs, and deliver better digital document experiences.
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Tensorlake
Tensorlake serves as a cutting-edge AI data cloud that efficiently converts unstructured data into formats suitable for AI applications. It adeptly transforms various content types, including documents, images, and presentations, into structured JSON or markdown segments that facilitate easy retrieval and analysis by large language models. The document ingestion APIs are capable of handling a wide range of file types, from handwritten notes to PDFs and intricate spreadsheets, while executing post-processing tasks such as chunking and preserving the original reading order and layout. With its serverless workflows, Tensorlake provides rapid end-to-end data processing, empowering users to create and implement fully managed Workflow APIs in Python that can scale down to zero when not in use and seamlessly scale up during data processing tasks. Additionally, it is designed to process millions of documents simultaneously, ensuring that context and interrelations among different data formats are preserved, while also offering robust, role-based access control to enhance team collaboration. This flexibility and efficiency make Tensorlake an invaluable tool for organizations looking to streamline their AI data preparation processes.
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MPS IntelliVector
Extracting business information from various sources such as printed or handwritten documents, forms, checks, invoices, emails, and more is a crucial task. This process can automatically convert unstructured customer data into a structured and digital format that is ready for business use. Once processed, the valuable data can be exported seamlessly into enterprise systems, databases, lines of business, or integrated into existing workflows. Despite the ongoing digitization and automation trends, paper remains a prevalent component in business operations worldwide. Many large corporations and organizations continue to face challenges with disorganized physical and digital documents that hinder their workflow efficiency. Significant time and resources are often dedicated to implementing automated solutions that still necessitate human intervention for data processing, which can ultimately diminish productivity and inflate costs. Consequently, businesses frequently find themselves in a position where they must sacrifice either cost-effectiveness, speed, accuracy, or the confidentiality of their data. The need for an effective solution that addresses these issues is more pressing than ever.
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