ByteScout Text Recognition SDK
Text Recognition is the process of detecting and converting images or documents (e.g. PDF) that contain typed or printed text into a computer encoded text using OCR (Optical Character Recognition) process powered by Machine Learning and AI. Automates tedious tasks such as data entry from specific documents such as driver licenses, passports, receipts, technical documents, bank statements, etc. Functions to specify rectangular areas of an image those are subject to the recognition with optional rotation and flipping. We combine very sophisticated technologies with any tools youâll find on the website. We make our SDKs respond to your needs. If you are looking for tutorials and explanations, source codes and documentation will give you a better understanding of what is going on.
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GLM-OCR
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoderâdecoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale imageâtext data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.
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DeepSeek-V2
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.
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DeepSeek-VL
DeepSeek-VL is an open source Vision-Language (VL) model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios, including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead.
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