Model overview
**Qalam-Net V2** (قلم-نت) is a high-performance Arabic Optical Character Recognition (OCR) system. Built on the **TrOCR** (Transformer-based OCR) architecture, it achieves superior accuracy by treats OCR as a sequence-to-sequence problem, mapping visual features directly to text tokens. The model utilizes a **Vision-Encoder-Decoder** framework, specifically optimized for the complexities of Arabic script (ligatures, cursive nature, and right-to-left orientation). - **End-to-End Transformer**: No reliance on traditional CNN-RNN architectures or complex preprocessing (like line segmentation). - **Arabic Script Specialist**: Fine-tuned on the `mssqpi/Arabic-OCR-Dataset` for robust handling of various Arabic fonts and styles. - **State-of-the-Art Accuracy**: Leverages pre-trained vision and language weights from `microsoft/trocr-base-handwritten`. - **Flexible Deployment**: Supports CUDA, MPS (Apple Silicon), and CPU execution.
Key features
特点摘要待依据来源补齐,不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。