Aller au contenu
ModelPrésentéExterne
Télécharger

Tzefa Word OCR TrOCR

Fine-tuned TrOCR model for recognizing individual handwritten Tzefa keywords.

TypeOtherVersionRevision 4432d4eCClasse

Source du modèle

Type
Other
Taille du fichier
235 MB
Version
vRevision 4432d4e
Mis à jour
18 sept. 2026
Téléchargements
12
Source
Hugging Face

Description de la source

Tzefa Word OCR Model (Fine-tuned TrOCR)

Fine-tuned TrOCR model for recognizing individual handwritten Tzefa keywords.

Architecture

  • Base model: microsoft/trocr-small-stage1
  • Fine-tuned on: Handwritten Tzefa keywords (uppercase Hebrew programming commands, number words, variable names)
  • Framework: HuggingFace Transformers (VisionEncoderDecoderModel)

Vocabulary

Tzefa keywords include:

  • Commands: MAKEINTEGER, MAKESTR, MAKELIST, MAKEBOOL, PRINTINTEGER, PRINTSTR, ADD, SUBTRACT, MULTIPLY, DIVIDE, WHILETRUE, IFTRUE, etc.
  • Number words: ZERO through ONEHUNDRED
  • User-defined variable names

Usage

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

# use_fast=False is required to prevent tokenizer conversion crash
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-stage1", use_fast=False)
model = VisionEncoderDecoderModel.from_pretrained("WARAJA/Tzefa-Word-OCR-TrOCR")

image = Image.open("word_crop.png").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values

generated_ids = model.generate(pixel_values)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(text)

Related

Sources

1 source
Hugging FacePrincipalVersion 4432d4ed7139bee3e1d26ca8c0b57107f949233f
huggingface.co/WARAJA/Tzefa-Word-OCR-TrOCR/tree/4432d4ed7139bee3e1d26ca8c0b57107f949233f

Vérifié 3 sept.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 235 MB · SHA-256 e94e83c4e17b…b9fa · Hugging Face

Download file

Extraits de sources

2 extraits

--- license: cc-by-nc-3.0 pipeline_tag: image-to-text tags: - vision - ocr - trocr - transformers - handwriting-recognition - document-processing --- # Tzefa Word OCR Model (Fine-tuned TrOCR) Fine-tuned TrOCR model for recognizing individual handwritten Tzefa keywords. ## Architecture - **Base model:** `microsoft/trocr-small-stage1` - **Fine-tuned on:** Handwritten Tzefa keywords (uppercase Hebrew programming commands, number words, variable names) - **Framework:** HuggingFace Transformers (`VisionEncoderDecoderModel`) ## Vocabulary Tzefa keywords include: - **Commands:** MAKEINTEGER, MAKESTR, MAKELIST, MAKEBOOL, PRINTINTEGER, PRINTSTR, ADD, SUBTRACT, MULTIPLY, DIVIDE, WHILETRUE, IFTRUE, etc. - **Number words:** ZERO through ONEHUNDRED - **User-defined variable names** ## Usage ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image # use_fast=False is required to prevent tokenizer conversion crash processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-stage1", use_fast=False) model = VisionEncoderDecoderModel.from_pretrained("WARAJA/Tzefa-Word-OCR-TrOCR") image = Image.open("word_crop.png").convert("RGB") pixel_values = processor(image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(text) ``` ## Related - [Full Tzefa Pipeline](https://huggingface.co/spaces/WARAJA/Tzefa) - [OCR Demo](https://huggingface.co/spaces/WARAJA/Tzefa-OCR) - [Word OCR Dataset](https://huggingface.co/datasets/WARAJA/Tzefa-Word-OCR-Dataset)

Source context: 12 downloads · 1 likes · Pipeline image-to-text · Library transformers · Repo WARAJA/Tzefa-Word-OCR-TrOCR