LogicalDOC empowers organizations all over the globe to take complete control of their document management. This premier document management system (DMS), which focuses on business process automation and quick content retrieval, allows teams to create, collaborate and manage large volumes of documents. It also stores valuable company data in one central repository. The system features include drag-and-drop document uploads, forms management, optical characters recognition (OCR), duplicate detection and barcode recognition, event logs, document archiving and integrated document workflow.
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ARGOS Identity provides AI-powered identity verification, fraud prevention, KYB, and workflow automation solutions for businesses operating in regulated and high-risk industries.
ARGOS ID Check enables organizations to verify customers remotely using identity document authentication, facial recognition, selfie verification, liveness detection, AML screening, age verification, and additional fraud signals. The platform supports identity documents from more than 200 countries and is designed for fintech, gaming, virtual assets, e-commerce, telecommunications, and other digital platforms.
Businesses can configure their verification process by selecting the modules that match their compliance requirements and risk tolerance. ARGOS can help identify forged documents, deepfakes, duplicate users, bots, VPNs, and suspicious activity while maintaining a fast and straightforward onboarding experience.
For business verification, ARGOS Omni automates KYB and compliance workflows. Omni supports document collection and extraction, business registry searches, ownership and UBO identification, AML screening, case management, and audit reporting. Custom workflows and decision rules help teams reduce manual reviews and apply compliance policies consistently.
ARGOS gives businesses one flexible platform for verifying people and companies, preventing fraud, lowering operational costs, and scaling compliant onboarding.
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Yandex Vision
Yandex Vision OCR is capable of identifying and extracting text from images while also adding automatic punctuation to the output. This advanced service can automatically recognize and support over 50 languages. It efficiently extracts standard fields and processes text from various templates and documents, including passports, driver’s licenses, vehicle registration certificates, and license plates. The system is proficient in handling both Russian and English languages, accommodating combinations of handwritten and printed texts seamlessly. It also intelligently analyzes table structures, delivering text in organized row and column formats. In addition to optical character recognition (OCR) and document identification, it includes functionalities for recognizing license plate numbers. Yandex Vision OCR supports file formats such as JPEG, PNG, and PDF, with a maximum file size limit of 20 MB and up to 300 pages per document. Notably, the service can effectively scan images to locate passports from 20 different countries, along with various types of driver’s licenses, vehicle registration papers, and license plates, making it a versatile tool for document processing. Overall, it enhances efficiency in text recognition tasks across a wide range of applications.
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GLM-OCR
GLM-OCR is an advanced multimodal optical character recognition system and an open-source framework that excels in delivering precise, efficient, and thorough document comprehension by integrating textual and visual elements within a cohesive encoder-decoder design inspired by the GLM-V series. This model features a visual encoder that has been pre-trained on extensive image-text datasets alongside a streamlined cross-modal connector that channels information into a GLM-0.5B language decoder. It offers capabilities for layout detection, simultaneous recognition of various regions, and structured outputs for diverse content types, including text, tables, formulas, and intricate real-world document formats. Furthermore, it employs Multi-Token Prediction (MTP) loss and robust full-task reinforcement learning techniques to enhance training efficiency, boost recognition accuracy, and improve generalization across various tasks, leading to remarkable performance on significant document understanding challenges. This innovative approach not only sets new benchmarks but also opens up possibilities for further advancements in the field of document analysis.
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