Baseer OCR V1.0

--- license: apache-2.0 language: - ar - en base_model: - Qwen/Qwen2-VL-2B-Instruct pipeline_tag: image-to-text tags: - legal --- # Baseer OCR V1.0 - Arabic Document OCR Model <p align="center"> <img src="https://img.shields.io/badge/Python-3.10+-blue.svg" alt="Python"> <img src="https://img.shields.io/badge/Transformers-5.3.0+-blue.svg" alt="Transformers"> <img src="https://img.shields.io/badge/License-Apache%202.0-green.svg" alt="License"> <img src="https://img.shields.io/badge/Vision-Language-Model-red.svg" alt="VLM"> </p> **Baseer OCR V1.0** is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) specifically designed for Optical Character Recognition (OCR) on complex Arabic legal documents. ## 🎯 Model Overview | Attribute | Value | |-----------|-------| | **Base Model** | Qwen2-VL-2B-Instruct | | **Fine-tuning Method** | LoRA (Rank: 48) | | **Training Framework** | LlamaFactory | | **Model Type** | Vision-Language Model (VLM) | | **License** | Apache 2.0 | | **Release Date** | March 2025 | ## 📋 Model Description Baseer OCR (بصير - meaning "reader" in Arabic) is specialized for extracting text from Arabic legal documents with complex layouts. This model builds upon Qwen2-VL's strong vision-language capabilities and is fine-tuned to handle: - 📜 **Arabic Legal Documents** - Contracts, court documents, official papers - 🔤 **Complex Arabic Typography** - Various fonts, sizes, and styles - 📄 **Multi-column Layouts** - Documents with complex structuring - 🖼️ **Mixed Content** - Documents with tables, stamps, and annotations - 🔍 **Handwritten & Printed Text** - Both typed and handwritten Arabic ### Key Features - **JSON Structured Output** - Returns extracted text in structured JSON format - **Arabic Language Support** - Optimized for Arabic script recognition - **Vision-Language Understanding** - Combines visual understanding with language generation - **Instruction Following** - Responds to detailed extraction prompts ## 🚀 Quick Start ```python from transformers import Qwen2VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info from PIL import Image # Load the model model = Qwen2VLForConditionalGeneration.from_pretrained( "AbdoTarek/Baseer-OCR-V1.0", torch_dtype="auto", device_map="auto" ).eval() processor = AutoProcessor.from_pretrained("AbdoTare...

Source context: 162 downloads · 0 likes · Pipeline image-to-text · Repo AbdoTarek/Baseer-OCR-V1.0