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PaddleOCR-VL-1.5 Example

PaddleOCR-VL-1.5.mp4

Introduction

PaddleOCR-VL-1.5 is a unified Vision-Language OCR model that supports multiple document understanding tasks through a single model architecture. Built upon powerful vision-language foundations, it can handle diverse OCR scenarios including text recognition, table extraction, formula recognition, chart understanding, seal recognition, and text spotting with bounding boxes.

Here, we will show you how to use PaddleOCR-VL-1.5 on X-AnyLabeling to perform various OCR and document understanding tasks.

Let's get started!

Supported Tasks

PaddleOCR-VL-1.5 supports six distinct tasks:

Task Description Output
OCR Optical Character Recognition for text extraction Text content
Table Recognition Extract table structure and content HTML/Markdown table
Formula Recognition Recognize mathematical formulas LaTeX format
Chart Recognition Extract information from charts and graphs Structured data
Text Spotting Detect and recognize text with bounding boxes Polygon shapes with text
Seal Recognition Recognize seal stamps and chop marks Text content

PP-DocLayoutV3 Label Routing

When the PPOCR panel uses PP-DocLayoutV3 for layout detection and then sends each cropped block to PaddleOCR-VL-1.5, labels are routed as follows:

Routed Task PP-DocLayoutV3 Labels
OCR doc_title, paragraph_title, header, footer, content, reference, reference_content, text, vertical_text, aside_text, abstract, footnote, vision_footnote, figure_title, number
Table Recognition table
Formula Recognition inline_formula, display_formula, formula_number, algorithm
Chart Recognition chart
Seal Recognition seal
Image Only (no text task) image, header_image, footer_image

Note

PP-DocLayoutV3 officially provides 25 labels. X-AnyLabeling also routes an additional compatibility label formula to Formula Recognition when it appears in layout output.

Installation

You'll need to get X-AnyLabeling-Server up and running first. Check out the installation guide for the details. Make sure you're running at least v0.0.7 of the server and v3.3.9 of the X-AnyLabeling client, otherwise you might run into compatibility issues.

Important

PaddleOCR-VL-1.5 requires transformers>=5.0.0. Install it with:

python -m pip install "transformers>=5.0.0"

For more details, see the official model page.

Tip

We highly recommend installing flash-attn to boost performance and reduce memory usage:

pip install flash-attn --no-build-isolation

Once that's done, head over to configs/models.yaml and enable paddleocr_vl_1_5. There's an example config you can reference if you're not sure how to set it up.

You can tweak the settings in paddleocr_vl_1_5.yaml to fit your needs.

Configuration Parameters

Parameter Default Description
model_path PaddlePaddle/PaddleOCR-VL-1.5 HuggingFace model path
device cuda:0 Device for inference
torch_dtype bfloat16 Model precision
max_new_tokens 512 Maximum tokens for generation
max_pixels 1605632 Maximum pixels for text tasks (1280×28×28)
spotting_max_pixels 1605632 Maximum pixels for spotting task (2048×28×28)
spotting_upscale_threshold 1500 Threshold for image upscaling in spotting

Note

If inference times out, try adjusting max_new_tokens, max_pixels, spotting_max_pixels, and spotting_upscale_threshold based on your GPU memory.

Getting Started

Launch the X-AnyLabeling client, press Ctrl+A or click the AI button in the left menu bar to open the auto-labeling panel. In the model dropdown list, select Remote-Server, then choose PaddleOCR-VL-1.5.

OCR (Text Recognition)

The OCR task extracts text content from images.

Usage:

  1. Select the "OCR" task from the task dropdown
  2. Click the "Run" button to extract text
  3. The recognized text will be displayed in the description field

Table Recognition

The Table Recognition task extracts table structure and content from document images.

Usage:

  1. Select the "Table Recognition" task from the task dropdown
  2. Click the "Run" button to extract table content
  3. The result will be formatted as HTML/Markdown table structure

Formula Recognition

The Formula Recognition task recognizes mathematical formulas and converts them to LaTeX format.

Usage:

  1. Select the "Formula Recognition" task from the task dropdown
  2. Click the "Run" button to recognize formulas
  3. The result will be in LaTeX format

Chart Recognition

The Chart Recognition task extracts information from charts and graphs.

Usage:

  1. Select the "Chart Recognition" task from the task dropdown
  2. Click the "Run" button to analyze the chart
  3. The extracted data will be displayed in structured format

Text Spotting

The Text Spotting task detects text regions and recognizes their content with polygon bounding boxes.

Usage:

  1. Select the "Text Spotting" task from the task dropdown
  2. Click the "Run" button to detect and recognize text
  3. Polygon shapes with recognized text will be created on the canvas

Tip

For small images (width and height both less than 1500 pixels), the model automatically upscales the image by 2x for better detection accuracy. You can adjust this threshold via spotting_upscale_threshold.

Seal Recognition

The Seal Recognition task recognizes text from seal stamps and chop marks.

Usage:

  1. Select the "Seal Recognition" task from the task dropdown
  2. Click the "Run" button to recognize seal text
  3. The recognized text will be displayed in the description field

Tip

All tasks support batch processing. You can run inference on the entire dataset with a single click using Ctrl+M or the batch processing feature in X-AnyLabeling.

Related Models

  • PP-DocLayoutV3: Document layout analysis model that works well with PaddleOCR-VL-1.5