--- title: "waqf-ocr-hand-written-v1" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/78309b31-df5b-408b-acef-28a933a8d0a0" updated: "2026-11-13" --- # waqf-ocr-hand-written-v1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads handwritten Arabic text from images and returns it as text. A fine-tuned PaddleOCR-VL model for people digitizing Arabic handwriting. waqf-ocr-hand-written-v1 is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Waqf-AI ## About ### What it is This is an optical character recognition model for handwritten Arabic. It was fine-tuned from PaddleOCR-VL-1.6 on a set of augmented Arabic handwriting images. You give it a picture of a line of handwriting and it returns the text. ### Who it suits It suits people who need Arabic handwriting turned into editable text, and researchers who work on Arabic handwriting recognition. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Archives and libraries | Digitization teams | Convert handwritten Arabic pages into searchable text | Scanning a handwritten notebook into text | | Education | Teachers and students | Transcribe handwritten Arabic notes or exercises | Turning a handwritten lesson into a text file | | Research | Document analysis researchers | Use the model as a baseline for handwriting recognition | Comparing it with another vision model on handwritten lines | | Publishing | Editors | Get a first draft of text from handwritten manuscripts | Preparing a manuscript page for proofreading | You can skip it if your text is printed rather than handwritten, or is in a language other than Arabic. ### Where to use it - Python code that uses the transformers library and PIL for images - Windows, macOS or Linux, with Python for the download - Batch jobs over scanned lines of handwriting | Use case | Who | What they get back | | --- | --- | --- | | Transcribe a handwritten line | Anyone with scans | The Arabic text of the image | | Digitize handwritten documents | Archives | Text ready to search or proofread | | Benchmark an OCR model | Researchers | Error rates to compare against other models | ### How to set it up - Python on your computer - The transformers library, PyTorch and the PIL imaging package - Disk space, about 1.9 GB for the half-precision weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Load the processor and model in Python with trust\_remote\_code turned on. ```sh pip install -U huggingface_hub hf download Waqf-AI/waqf-ocr-hand-written-v1 ``` ### How to use it You open an image of handwriting, load the processor and model through transformers, and ask the model to read the image. The part of the card shown here begins the usage code like this. ```python from PIL import Image import torch from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Waqf-AI/written_ocr_paddle1.6", trust_remote_code=True) ``` ### Example A person has a scanned image of one line of handwritten Arabic and loads it with PIL. They pass it to the model. What comes back is the Arabic text of that line. The card shows sample lines where the output matched the reference exactly. ### For newcomers and experts If you are new to this, begin with one clean image of a single line of handwriting, because the model was tested on text lines. Compare the output with the image yourself. If you know the field, the card tests on 200 held-out handwritten line images from the same augmented dataset. It reports a word error rate of 0.0077 and a character error rate of 0.0056, against 0.8057 and 0.3690 for the base PaddleOCR-VL-1.6 and higher rates for the Qwen vision models it tried. Scan or photograph a line of handwriting → Load the image with PIL → Load the processor and model → The model reads the image → Read the Arabic text it returns ### Good to know | Model | Word error rate | Character error rate | | --- | --- | --- | | PaddleOCR-VL-1.6 base | 0.8057 | 0.3690 | | Qwen2.5-VL-7B-Instruct | 0.6322 | 0.3292 | | This fine-tuned model | 0.0077 | 0.0056 | - Limits: the scores come from 200 line images of the same kind as the training data, so other handwriting may do worse. - Trust: the usage code turns on trust\_remote\_code, which runs code from the model repository. - Language: the record lists Arabic only. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the model files to your computer and reads the images you give it. ## README ````markdown # waqf-ocr-hand-written-v1 Reads handwritten Arabic text from images and returns it as text. A fine-tuned PaddleOCR-VL model for people digitizing Arabic handwriting. Made by Waqf-AI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is an optical character recognition model for handwritten Arabic. It was fine-tuned from PaddleOCR-VL-1.6 on a set of augmented Arabic handwriting images. You give it a picture of a line of handwriting and it returns the text. Who it suits: It suits people who need Arabic handwriting turned into editable text, and researchers who work on Arabic handwriting recognition. ```table Industry | Who uses it | How they use it | Example Archives and libraries | Digitization teams | Convert handwritten Arabic pages into searchable text | Scanning a handwritten notebook into text Education | Teachers and students | Transcribe handwritten Arabic notes or exercises | Turning a handwritten lesson into a text file Research | Document analysis researchers | Use the model as a baseline for handwriting recognition | Comparing it with another vision model on handwritten lines Publishing | Editors | Get a first draft of text from handwritten manuscripts | Preparing a manuscript page for proofreading ``` You can skip it if your text is printed rather than handwritten, or is in a language other than Arabic. Where to use it: - Python code that uses the transformers library and PIL for images - Windows, macOS or Linux, with Python for the download - Batch jobs over scanned lines of handwriting ```table Use case | Who | What they get back Transcribe a handwritten line | Anyone with scans | The Arabic text of the image Digitize handwritten documents | Archives | Text ready to search or proofread Benchmark an OCR model | Researchers | Error rates to compare against other models ``` How to set it up: - Python on your computer - The transformers library, PyTorch and the PIL imaging package - Disk space, about 1.9 GB for the half-precision weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Load the processor and model in Python with trust_remote_code turned on. ```sh pip install -U huggingface_hub hf download Waqf-AI/waqf-ocr-hand-written-v1 ``` How to use it: You open an image of handwriting, load the processor and model through transformers, and ask the model to read the image. The part of the card shown here begins the usage code like this. ```python from PIL import Image import torch from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Waqf-AI/written_ocr_paddle1.6", trust_remote_code=True) ``` Example: A person has a scanned image of one line of handwritten Arabic and loads it with PIL. They pass it to the model. What comes back is the Arabic text of that line. The card shows sample lines where the output matched the reference exactly. For newcomers and experts: If you are new to this, begin with one clean image of a single line of handwriting, because the model was tested on text lines. Compare the output with the image yourself. If you know the field, the card tests on 200 held-out handwritten line images from the same augmented dataset. It reports a word error rate of 0.0077 and a character error rate of 0.0056, against 0.8057 and 0.3690 for the base PaddleOCR-VL-1.6 and higher rates for the Qwen vision models it tried. ```flow Scan or photograph a line of handwriting Load the image with PIL Load the processor and model The model reads the image Read the Arabic text it returns ``` Good to know: ```table Model | Word error rate | Character error rate PaddleOCR-VL-1.6 base | 0.8057 | 0.3690 Qwen2.5-VL-7B-Instruct | 0.6322 | 0.3292 This fine-tuned model | 0.0077 | 0.0056 ``` - Limits: the scores come from 200 line images of the same kind as the training data, so other handwriting may do worse. - Trust: the usage code turns on trust_remote_code, which runs code from the model repository. - Language: the record lists Arabic only. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the model files to your computer and reads the images you give it. ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 959M - Weights: about 1.9 GB in F16; about 479 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 266 - Likes: 7 - License: apache-2.0 - Languages: ar - Tags: safetensors, paddleocr_vl, paddleocr, arabic, handwritten, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Waqf-AI/waqf-ocr-hand-written-v1 - Files: https://huggingface.co/Waqf-AI/waqf-ocr-hand-written-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Waqf-AI/waqf-ocr-hand-written-v1 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Waqf-AI/waqf-ocr-hand-written-v1 on Hugging Face](https://huggingface.co/Waqf-AI/waqf-ocr-hand-written-v1) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | waqf-ocr-hand-written-v1 (this page) | 266 | 7 | 959M | apache-2.0 | 2026-06-09 | | [PaddleOCR-VL](https://mindket.com/p/03a8220f-75d7-45bb-a395-caa387ddd320) | 12,780 | 1,685 | 959M | apache-2.0 | 2026-11-06 | | [PaddleOCR-VL-1.6](https://mindket.com/p/3fa47ed2-52f8-45fd-afc5-1286a5c4ef62) | 100,031 | 585 | 959M | apache-2.0 | 2026-11-06 | | [Chandra OCR 2](https://mindket.com/p/a6e4f077-438e-4d39-950e-b0d5bfc59501) | 1,356,074 | 606 | 5.3B | openrail | 2026-06-26 | | [Surya OCR 2.5](https://mindket.com/p/382aaad0-7644-4eca-ac3b-a85a0841b604) | 75,033 | 52 | 689M | openrail | 2026-10-29 | | [MinerU2.5-Pro](https://mindket.com/p/a614bc0f-436d-45d4-835f-e6d945cfb45d) | 37,179 | 115 | 1.2B | apache-2.0 | 2026-10-08 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-06-09 - First published: 2026-06-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Waqf-AI/waqf-ocr-hand-written-v1 on Hugging Face](https://huggingface.co/Waqf-AI/waqf-ocr-hand-written-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/78309b31-df5b-408b-acef-28a933a8d0a0) - [Its page on Mindket](https://mindket.com/p/78309b31-df5b-408b-acef-28a933a8d0a0)