| --- |
| 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: |
|
|
| [](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. |
|
|
