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)
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