beratkurar/hebrew_script_mode_classifier

Hebrew Script Mode Classifier

The Hebrew Script Mode Classifier is a deep learning model for classifying handwritten Hebrew document images into two script mode categories:

• Square
• Non-square

The accompanying inference interface also produces a spatial attention overlay showing the regions that received higher weights from the model's gated-attention pooling layer.

Model Details

Model Description

The model processes a handwritten Hebrew document image using a ConvNeXt feature extractor followed by masked gated-attention pooling and a binary classification head.

The pixel mask prevents padded image regions from contributing to the attention pooling operation. The output consists of class probabilities for square and non_square.

The public checkpoint is approximately 950 MB.

• Shared by: Tel Aviv University Computational Humanities (TAU-CH) GitHub organization
• Model type: Image classifier with a ConvNeXt backbone and masked gated-attention pooling
• Task: Hebrew script mode classification
• Language: Hebrew handwritten document images
• Number of classes: 2
• Classes: square, non_square
• Framework: PyTorch and timm
• Checkpoint format: PyTorch .pt
• Base architecture: ConvNeXt; the exact backbone configuration is stored in the checkpoint
• Default backbone fallback: convnext_base.fb_in22k_ft_in1k

Model Sources

• GitHub repository:
  https://github.com/TAU-CH/midrash_hebrew_script_mode_classifier

• Model repository:
  https://huggingface.co/beratkurar/hebrew_script_mode_classifier

• Interactive Colab demo:
  https://colab.research.google.com/github/TAU-CH/midrash_hebrew_script_mode_classifier/blob/main/Hebrew_Script_Mode_Classifier.ipynb

• Paper: TODO: add the publication link when available

Uses

Direct Use

The model is intended for classification of handwritten Hebrew document images into square and non-square script modes.

The provided Colab interface allows users to:

1. Start the inference environment.
2. Upload a handwritten Hebrew document image.
3. Receive class probabilities for square and non-square.
4. View an attention heatmap overlaid on the input image.

The simplest way to use the model is through the public Colab notebook:

Mixed-Script Documents

A page may contain multiple hands, scripts, annotations, marginalia, or mixed square and non-square writing. The model returns one page-level classification and does not explicitly model mixed-script content.

Image Cropping

Images larger than the configured maximum dimensions are center-cropped. Relevant evidence near the page boundaries may therefore be excluded.

The public inference code uses checkpoint-configured maximum dimensions, with a fallback of 2500 × 2500 pixels.