Multimodal Table Scanning Applications

Note: Due to performance limitations, Raspberry Pi 5 2GB, 4GB, RDK X5 4GB, and Jetson Orin Nano 4GB versions cannot run offline. Please refer to online tutorials for large models.

1. Concept Introduction

1.1 What is "Multimodal Table Scanning"?

Multimodal table scanning is a technology that uses image processing and artificial intelligence to identify and extract table information from images or PDF documents. It not only focuses on visual table structure recognition but also combines multimodal data such as text content and layout information to enhance table understanding. Large Language Models (LLMs) provide powerful semantic analysis capabilities for understanding this extracted information. The two complement each other, jointly improving the intelligence level of document processing.

1.2 Brief Description of Implementation Principles

1. Table Detection and Content Recognition

• Using computer vision technology to locate tables in documents and using OCR technology to convert the text within the tables into an editable format.
• Employs deep learning methods to parse table structure (row and column division, cell merging, etc.) and generate structured data representation.

2. Multimodal Fusion

• Integrates visual data (e.g., table layout), text (OCR results), and potential metadata (e.g., file type, source) to form a comprehensive data view.
• Uses a specially designed multimodal model (e.g., LayoutLM) to process these different types of data simultaneously for a more accurate understanding of table content and its contextual relationships.

2. Code Analysis

Key Code

1. Utility Layer Entry Point (largemodel/utils/tools_manager.py)

The scan_table function in this file defines the execution flow of the tool, specifically how it constructs a Prompt that requests a Markdown format return.

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# From largemodel/utils/tools_manager.py
class ToolsManager:
    # ...
    def scan_table(self, args):
        """
        Scan a table from an image and save the content as a Markdown file.
​
        :param args: Arguments containing the image path.
        :return: Dictionary with file path and content.
        """
        self.node.get_logger().info(f"Executing scan_table() tool with args: {args}")
        try:
            image_path = args.get("image_path")
            # ... (Path checking and fallback)
​
            # Construct a prompt asking the large model to recognize the table and return it in Markdown format.
            if self.node.language == 'zh':
                prompt = "请仔细分析这张图片，识别其中的表格，并将其内容以Markdown格式返回。"
            else:
                prompt = "Please carefully analyze this image, identify the table within it, and return its content in Markdown format."
​
            result = self.node.model_client.infer_with_image(image_path, prompt)
            
            # ... (Extract Markdown text from the results)
​
            # Save the recognized content to a Markdown file. 
            md_file_path = os.path.join(self.node.pkg_path, "resources_file", "scanned_tables", f"table_{timestamp}.md")
            with open(md_file_path, 'w', encoding='utf-8') as f:
                f.write(table_content)
​
            return {
                "file_path": md_file_path,
                "table_content": table_content
            }
        # ... (Error Handling)

22. Model Interface Layer (largemodel/utils/large_model_interface.py)

The infer_with_image function in this file serves as the unified entry point for all image-related tasks.

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# From largemodel/utils/large_model_interface.py
​
class model_interface:
    # ...
    def infer_with_image(self, image_path, text=None, message=None):
        """Unified image inference interface. """
        # ... (Prepare Message)
        try:
            # Determine which specific implementation to call based on the value of self.llm_platform
            if self.llm_platform == 'ollama':
                response_content = self.ollama_infer(self.messages, image_path=image_path)
            elif self.llm_platform == 'tongyi':
                # ... Logic for calling the Tongyi model
                pass
            # ... (Logic of other platforms)
        # ...
        return {'response': response_content, 'messages': self.messages.copy()}

Code Analysis

The table scanning function is a typical application of converting unstructured image data into structured text data. Its core technology remains guiding model behavior through Prompt Engineering.

1. Tool Layer (tools_manager.py):

• The scan_table function is the business process controller for this function. It receives an image containing a table as input.
• The most crucial operation of this function is constructing a targeted Prompt. This Prompt directly instructs the large model to perform two tasks: 1. Recognize the table in the image. 2. Return the recognized content in Markdown format. This requirement for the output format is key to achieving the unstructured to structured conversion.
• After constructing the Prompt, it calls the infer_with_image method of the model interface layer, passing the image and this formatting instruction along with it.
• After receiving the returned Markdown text from the model interface layer, it performs a file operation: writing the text content into a new .md file.

Finally, it returns structured data containing the new file paths and table contents.

2. Model Interface Layer (large_model_interface.py):

The infer_with_image function continues to act as a unified "dispatch center." It receives the image and prompt from scan_table and dispatches the task to the correct backend model implementation based on the current system configuration (self.llm_platform).

Regardless of the backend model, this layer's task is to handle the communication details with the specific platform, ensuring that image and text data are sent correctly, and then return the plain text (in this case, Markdown format) returned by the model to the utility layer.

In summary, the general process for table scanning is as follows: ToolsManager receives the image and constructs a command to "convert the table in this image to Markdown" -> ToolsManager calls the model interface -> model_interface packages the image and the command and sends it to the appropriate model platform according to the configuration -> The model returns Markdown formatted text -> model_interface returns the text to ToolsManager -> ToolsManager saves the text as a .md file and returns the result. This process demonstrates how to leverage the formatting capabilities of large models as a powerful OCR (Optical Character Recognition) and data structuring tool.

3. Practical Operation

3.1 Configuring Offline Large Models

3.1.1 Configuring the LLM Platform (yahboom.yaml)

This file determines which large model platform the model_service node loads as its primary language model.

Raspberry Pi 5 requires entering a Docker container; RDK X5 and Orin mainframes do not require this:

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./ros2_docker.sh

If you need to enter other commands within the same Docker container later, simply type ./ros2_docker.sh again in the host machine's terminal.

1. Open the file in the terminal:

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   vim ~/yahboom_ws/src/largemodel/config/yahboom.yaml
   

2. Modify/Confirm llm_platform:

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   model_service:                          #Model server node parameters
     ros__parameters:
       language: 'zh'                      #Large Model Interface Language
       useolinetts: True                   #This item is invalid in text mode and can be ignored
   ​
       # Large model configuration
       llm_platform: 'ollama'              # KEY: Make sure this is 'ollama'
       regional_setting : "China"
       camera_type: 'csi'                 # Camera type: 'csi', 'usb'
       mjpeg_stream_url: 'http://172.17.0.1:8080/camera.mjpg' # MJPEG stream URL for CSI camera in Docker 
   

3.1.2 Configuring the Model Interface (large_model_interface.yaml)

This file defines which vision model to use when the platform is selected as ollama.

1. Open the file in a terminal.

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vim ~/yahboom_ws/src/largemodel/config/large_model_interface.yaml

2.Find the ollama-related configuration.

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#.....
## Offline Large Language Models
# Ollama Configuration
ollama_host: "http://localhost:11434" # Ollama server address
ollama_model: "llava" # Important: Change this to the multimodal model you downloaded, such as "llava"
#.....

Note: Please ensure that the model specified in the configuration parameters (e.g., llava) can handle multimodal input.

3.2 Starting and Testing the Function (Text Mode)

Note: The performance will be poor when using a model with a small number of parameters or when running at a very low memory usage. For a better experience with this feature, please refer to the corresponding section in <Online Large Model (Text Interaction)>.

1. Prepare a table image file: Place a table image file to test in the following path: ~/yahboom_ws/src/largemodel/resources_file/scan_table Then name the image test_table.jpg
2. Start the largemodel main program (text mode): Open a terminal and run the following command:

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ros2 launch largemodel largemodel_control.launch.py text_chat_mode:=true

3. Send a text command: Open another terminal and run the following command:

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ros2 run text_chat text_chat

Then start typing: "Analyze the table."

4. Observation Results: In the first terminal running the main program, you will see log output indicating that the system received the command, called the scan_table tool, and completed the scan_table execution, saving the scanned information to a document. This document can be found in the ~/yahboom_ws/src/largemodel/resources_file/scan_table directory.

4. Common Problems and Solutions

Problem 1: Incomplete recognition of table content or typos.

Solution:

1. Image Quality: Ensure that the input table image is clear, undistorted, and evenly lit. Poor image quality is the most common cause of recognition errors.
2. Model Selection: Models with larger parameters tend to perform better. Try switching to a more powerful recognition model.

Problem 2: "Table file not found" error

Solution:

1. Check the path: Ensure you have placed the table image file in the specified path, named it test_table.jpg, and have read permissions for the file.
2. Image format: Ensure the image file is not corrupted and is in .jpg format.