Interface IDocumentClassifier<T>

Namespace

AiDotNet.Document.Interfaces

Assembly

AiDotNet.dll

Interface for document classification models.

public interface IDocumentClassifier<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.

Inherited Members

IDocumentModel<T>.ExpectedImageSize

IDocumentModel<T>.MaxSequenceLength

IDocumentModel<T>.RequiresOCR

IDocumentModel<T>.SupportedDocumentTypes

IDocumentModel<T>.IsOnnxMode

IDocumentModel<T>.EncodeDocument(Tensor<T>)

IDocumentModel<T>.ValidateInputShape(Tensor<T>)

IDocumentModel<T>.GetModelSummary()

IFullModel<T, Tensor<T>, Tensor<T>>.DefaultLossFunction

IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>.Train(Tensor<T>, Tensor<T>)

IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>.Predict(Tensor<T>)

IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>.GetModelMetadata()

IModelSerializer.Serialize()

IModelSerializer.Deserialize(byte[])

IModelSerializer.SaveModel(string)

IModelSerializer.LoadModel(string)

ICheckpointableModel.SaveState(Stream)

ICheckpointableModel.LoadState(Stream)

IParameterizable<T, Tensor<T>, Tensor<T>>.GetParameters()

IParameterizable<T, Tensor<T>, Tensor<T>>.SetParameters(Vector<T>)

IParameterizable<T, Tensor<T>, Tensor<T>>.ParameterCount

IParameterizable<T, Tensor<T>, Tensor<T>>.WithParameters(Vector<T>)

IFeatureAware.GetActiveFeatureIndices()

IFeatureAware.SetActiveFeatureIndices(IEnumerable<int>)

IFeatureAware.IsFeatureUsed(int)

IFeatureImportance<T>.GetFeatureImportance()

ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>.DeepCopy()

ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>.Clone()

IGradientComputable<T, Tensor<T>, Tensor<T>>.ComputeGradients(Tensor<T>, Tensor<T>, ILossFunction<T>)

IGradientComputable<T, Tensor<T>, Tensor<T>>.ApplyGradients(Vector<T>, T)

IJitCompilable<T>.ExportComputationGraph(List<ComputationNode<T>>)

IJitCompilable<T>.SupportsJitCompilation

Extension Methods

DistributedExtensions.AsDistributedForHighBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributedForLowBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, IShardingConfiguration<T>)

Remarks

Document classification models categorize documents into predefined classes such as invoices, forms, letters, scientific papers, etc.

For Beginners: Document classification is like sorting mail into different piles. The model looks at a document and decides what type it is. This is useful for: - Organizing scanned documents - Routing documents to appropriate processing pipelines - Quality control in document processing

Example usage:

var classifier = new DocumentTypeClassifier<float>(architecture);
var result = classifier.ClassifyDocument(documentImage);
Console.WriteLine($"Document type: {result.PredictedCategory}");

Properties

AvailableCategories

Gets the available classification categories for this model.

IReadOnlyList<string> AvailableCategories { get; }

Property Value

IReadOnlyList<string>

Methods

ClassifyDocument(Tensor<T>)

Classifies a document image into predefined categories.

DocumentClassificationResult<T> ClassifyDocument(Tensor<T> documentImage)

Parameters

documentImage Tensor<T>

The document image tensor.

Returns

DocumentClassificationResult<T>

Classification result with predicted category and confidence.

ClassifyDocument(Tensor<T>, int)

Classifies a document and returns top-K predictions.

DocumentClassificationResult<T> ClassifyDocument(Tensor<T> documentImage, int topK)

Parameters

documentImage Tensor<T>

The document image tensor.

topK int

Number of top predictions to return.

Returns

DocumentClassificationResult<T>

Classification result with top-K predictions.