---
language:
- he
library_name: timm
license: mit
pipeline_tag: image-classification
tags:
- hebrew-manuscripts
- hebrew-paleography
- document-image-analysis
- script-classification
- computational-humanities
- digital-humanities
- attention-visualization
- convnext
---

# 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:

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/TAU-CH/midrash_hebrew_script_mode_classifier/blob/main/Hebrew_Script_Mode_Classifier.ipynb)

### 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.