Table of Contents

Class TableTransformer<T>

Namespace
AiDotNet.Document.Analysis.TableDetection
Assembly
AiDotNet.dll

TableTransformer for table detection and structure recognition using DETR-style architecture.

public class TableTransformer<T> : DocumentNeuralNetworkBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable, ITableExtractor<T>, IDocumentModel<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance
TableTransformer<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

TableTransformer is based on the DETR (DEtection TRansformer) architecture, adapted for table detection and table structure recognition. It can detect tables in documents and identify their internal structure (rows, columns, cells, headers).

For Beginners: TableTransformer helps computers understand tables in documents. It can: 1. Find where tables are located in a page (table detection) 2. Identify the structure within tables - rows, columns, and cells (structure recognition) 3. Handle both bordered and borderless tables

Example usage:

var tableModel = new TableTransformer<float>(architecture);
var tables = tableModel.DetectTables(documentImage);
foreach (var table in tables)
{
    var structure = tableModel.RecognizeStructure(table.Image);
    Console.WriteLine($"Table has {structure.NumRows} rows and {structure.NumColumns} columns");
}

Reference: "PubTables-1M: Towards Comprehensive Table Extraction from Unstructured Documents" (CVPR 2022) https://arxiv.org/abs/2110.00061

Constructors

TableTransformer(NeuralNetworkArchitecture<T>, int, int, int, int, int, int, IOptimizer<T, Tensor<T>, Tensor<T>>?, ILossFunction<T>?)

Creates a TableTransformer model using native layers for training and inference.

public TableTransformer(NeuralNetworkArchitecture<T> architecture, int imageSize = 800, int hiddenDim = 256, int numEncoderLayers = 6, int numDecoderLayers = 6, int numHeads = 8, int numQueries = 100, IOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ILossFunction<T>? lossFunction = null)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture.

imageSize int

Expected input image size (default: 800).

hiddenDim int

Transformer hidden dimension (default: 256).

numEncoderLayers int

Number of encoder layers (default: 6).

numDecoderLayers int

Number of decoder layers (default: 6).

numHeads int

Number of attention heads (default: 8).

numQueries int

Number of object queries (default: 100).

optimizer IOptimizer<T, Tensor<T>, Tensor<T>>

Optimizer for training (optional).

lossFunction ILossFunction<T>

Loss function (optional).

Remarks

Default Configuration (from CVPR 2022 paper): - Backbone: ResNet-18 (for detection) or ResNet-50 (for structure) - Transformer: 6 encoder layers, 6 decoder layers, 256 hidden dim - Object queries: 100 - Image size: 800

TableTransformer(NeuralNetworkArchitecture<T>, string, string, int, int, int, int, int, int, IOptimizer<T, Tensor<T>, Tensor<T>>?, ILossFunction<T>?)

Creates a TableTransformer model using pre-trained ONNX models for inference.

public TableTransformer(NeuralNetworkArchitecture<T> architecture, string detectionModelPath, string structureModelPath, int imageSize = 800, int hiddenDim = 256, int numEncoderLayers = 6, int numDecoderLayers = 6, int numHeads = 8, int numQueries = 100, IOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ILossFunction<T>? lossFunction = null)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture.

detectionModelPath string

Path to the table detection ONNX model.

structureModelPath string

Path to the structure recognition ONNX model.

imageSize int

Expected input image size (default: 800).

hiddenDim int

Transformer hidden dimension (default: 256).

numEncoderLayers int

Number of encoder layers (default: 6).

numDecoderLayers int

Number of decoder layers (default: 6).

numHeads int

Number of attention heads (default: 8).

numQueries int

Number of object queries (default: 100).

optimizer IOptimizer<T, Tensor<T>, Tensor<T>>

Optimizer for training (optional).

lossFunction ILossFunction<T>

Loss function (optional).

Exceptions

ArgumentNullException

Thrown if model paths are null.

FileNotFoundException

Thrown if ONNX model files don't exist.

Properties

ExpectedImageSize

Gets the expected input image size (assumes square images).

public int ExpectedImageSize { get; }

Property Value

int

Remarks

Common values: 224 (ViT base), 384, 448, 512, 768, 1024. Input images will be resized to [ImageSize x ImageSize] before processing.

NumQueries

Gets the number of object queries used in DETR decoder.

public int NumQueries { get; }

Property Value

int

RequiresOCR

Gets whether this model requires OCR preprocessing.

public override bool RequiresOCR { get; }

Property Value

bool

Remarks

Layout-aware models (LayoutLM, etc.) require OCR to provide text and bounding boxes. OCR-free models (Donut, Pix2Struct) process raw pixels directly.

SupportedDocumentTypes

Gets the supported document types for this model.

public override DocumentType SupportedDocumentTypes { get; }

Property Value

DocumentType

SupportsBorderedTables

Gets whether this model supports bordered tables.

public bool SupportsBorderedTables { get; }

Property Value

bool

SupportsBorderlessTables

Gets whether this model supports borderless tables.

public bool SupportsBorderlessTables { get; }

Property Value

bool

SupportsMergedCells

Gets whether this model can detect merged cells (row/column spans).

public bool SupportsMergedCells { get; }

Property Value

bool

Methods

ApplyDefaultPostprocessing(Tensor<T>)

Applies TableTransformer's industry-standard postprocessing: pass-through (DETR outputs are already in final format).

protected override Tensor<T> ApplyDefaultPostprocessing(Tensor<T> modelOutput)

Parameters

modelOutput Tensor<T>

Returns

Tensor<T>

ApplyDefaultPreprocessing(Tensor<T>)

Applies TableTransformer's industry-standard preprocessing: COCO/ImageNet normalization.

protected override Tensor<T> ApplyDefaultPreprocessing(Tensor<T> rawImage)

Parameters

rawImage Tensor<T>

Returns

Tensor<T>

Remarks

TableTransformer uses COCO-style normalization with ImageNet mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. From the PubTables-1M paper (CVPR 2022).

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

DetectTables(Tensor<T>)

Detects tables in a document image.

public IEnumerable<TableRegion<T>> DetectTables(Tensor<T> documentImage)

Parameters

documentImage Tensor<T>

The document image tensor.

Returns

IEnumerable<TableRegion<T>>

List of detected table regions with bounding boxes.

DetectTables(Tensor<T>, double)

Detects tables with a custom confidence threshold.

public IEnumerable<TableRegion<T>> DetectTables(Tensor<T> documentImage, double confidenceThreshold)

Parameters

documentImage Tensor<T>
confidenceThreshold double

Returns

IEnumerable<TableRegion<T>>

Dispose(bool)

Disposes of resources used by this model.

protected override void Dispose(bool disposing)

Parameters

disposing bool

True if disposing managed resources.

EncodeDocument(Tensor<T>)

Processes a document image and returns encoded features.

public Tensor<T> EncodeDocument(Tensor<T> documentImage)

Parameters

documentImage Tensor<T>

The document image tensor [batch, channels, height, width] or [channels, height, width].

Returns

Tensor<T>

Encoded document features suitable for downstream tasks.

Remarks

For Beginners: This method converts a document image into a numerical representation (feature vector) that captures the document's content and structure. These features can then be used for tasks like classification, QA, or information extraction.

ExportTables(Tensor<T>, TableExportFormat)

Exports detected tables to a specific format.

public string ExportTables(Tensor<T> documentImage, TableExportFormat format)

Parameters

documentImage Tensor<T>

The document image tensor.

format TableExportFormat

Output format (CSV, JSON, HTML, Markdown, Excel).

Returns

string

Serialized table data in the specified format.

ExtractTableContent(Tensor<T>)

Extracts table content as structured data.

public IEnumerable<List<List<string>>> ExtractTableContent(Tensor<T> documentImage)

Parameters

documentImage Tensor<T>

The document image tensor.

Returns

IEnumerable<List<List<string>>>

Tables as lists of rows, where each row is a list of cell contents.

Remarks

This is a convenience method that combines table detection, structure recognition, and OCR to return the final table content.

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

GetModelSummary()

Gets a summary of the model architecture.

public string GetModelSummary()

Returns

string

A string describing the model's architecture, parameters, and capabilities.

InitializeLayers()

Initializes the layers of the neural network based on the architecture.

protected override void InitializeLayers()

Remarks

For Beginners: This method sets up all the layers in your neural network according to the architecture you've defined. It's like assembling the parts of your network before you can use it.

Predict(Tensor<T>)

Makes a prediction using the neural network.

public override Tensor<T> Predict(Tensor<T> input)

Parameters

input Tensor<T>

The input data to process.

Returns

Tensor<T>

The network's prediction.

Remarks

For Beginners: This is the main method you'll use to get results from your trained neural network. You provide some input data (like an image or text), and the network processes it through all its layers to produce an output (like a classification or prediction).

RecognizeStructure(Tensor<T>)

Recognizes the structure of a table (rows, columns, cells).

public TableStructureResult<T> RecognizeStructure(Tensor<T> tableImage)

Parameters

tableImage Tensor<T>

Cropped table image tensor (from DetectTables).

Returns

TableStructureResult<T>

Table structure with cell positions and spans.

RecognizeStructure(Tensor<T>, double)

Recognizes table structure with a custom confidence threshold.

public TableStructureResult<T> RecognizeStructure(Tensor<T> tableImage, double confidenceThreshold)

Parameters

tableImage Tensor<T>
confidenceThreshold double

Returns

TableStructureResult<T>

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input data.

expectedOutput Tensor<T>

The expected output for the given input.

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

UpdateParameters(Vector<T>)

Updates the network's parameters with new values.

public override void UpdateParameters(Vector<T> gradients)

Parameters

gradients Vector<T>

Remarks

For Beginners: During training, a neural network's internal values (parameters) get adjusted to improve its performance. This method allows you to update all those values at once by providing a complete set of new parameters.

This is typically used by optimization algorithms that calculate better parameter values based on training data.

ValidateInputShape(Tensor<T>)

Validates that an input tensor has the correct shape for this model.

public void ValidateInputShape(Tensor<T> documentImage)

Parameters

documentImage Tensor<T>

The tensor to validate.

Exceptions

ArgumentException

Thrown if the tensor shape is invalid.

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