Signature Recognition using CNN

Comparing CNN feature learning against classical HOG / SIFT for signatures.

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Domain
Computer Vision
Year
2025
Type
Research report — internal engineering work

This is internal engineering and R&D work, not a client project. It is published to show technical depth — the modelling, evaluation, and tooling behind the AI automation we build for clients. No client is associated with it.

A signature-recognition study that processes signature images, extracts features, and classifies signatures by individual ID. It benchmarks CNN-based feature extraction against traditional techniques (HOG and SIFT), with a full segmentation and train/test pipeline.

Tools & methods

  • Python
  • CNN
  • HOG
  • SIFT
  • OpenCV
Full technical report

Objectives

  • Segment and preprocess signature samples (normalize, denoise, isolate).
  • Classify signatures by signer identity.
  • Compare CNN feature extraction with HOG and SIFT.

Process

  • Collect a labelled signature dataset (16 images × 12 rows × 4 signatures).
  • Preprocess: normalize, reduce noise, segment signatures from background.
  • Train a CNN (conv + pooling + dense) and evaluate with accuracy, precision, recall and F-measure.
  • Generate confusion matrices for HOG and CNN.

Results

  • On this small dataset, HOG features gave the strongest scores; CNN highlighted the need for more data.
  • Regularization mitigated early overfitting.

What we took from it

  • Signature-style variability demands robust preprocessing.
  • Classical descriptors remain strong baselines on tiny datasets.

From the report

  • Confusion matrix — signature recognition (1)
    Confusion matrix — signature recognition (1)
  • Confusion matrix — signature recognition (2)
    Confusion matrix — signature recognition (2)

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