Chickenscratch Ocr
Fine-tuned TrOCR model for personal handwriting recognition.
Model source
- Kind
- CLIP vision
- File size
- 1.24 GB
- Version
- vmodel
- Updated
- Sep 26, 2026
- Downloads
- 8
- Tasks
- Captioning
- Source
- Hugging Face
Source description
Chickenscratch OCR
Fine-tuned TrOCR model for personal handwriting recognition.
Model Details
- Base Model: microsoft/trocr-base-handwritten
- Training Data: ~1,300 labeled handwritten lines from reMarkable tablet notes
- Performance: CER 24%, WER 51%
- Training: 15 epochs on Apple M4 Max
Usage
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
processor = TrOCRProcessor.from_pretrained("moonstripe/chickenscratch-ocr")
model = VisionEncoderDecoderModel.from_pretrained("moonstripe/chickenscratch-ocr")
# Load a handwritten line image
image = Image.open("line.png").convert("RGB")
# Process and predict
pixel_values = processor(images=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)
Training
This model was trained using the chickenscratch-ocr pipeline, which provides:
- Data collection from reMarkable tablet
- Web UI for labeling handwritten lines
- Fine-tuning script with Apple Silicon MPS support
Limitations
- Optimized for a specific person's handwriting
- Best results on single text lines (not full pages)
- May struggle with unusual symbols or very messy writing
Sources
1 source
Sources
1 sourceVerified Sep 10
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit tags: - trocr - ocr - handwriting - remarkable pipeline_tag: image-to-text --- # Chickenscratch OCR Fine-tuned [TrOCR](https://huggingface.co/microsoft/trocr-base-handwritten) model for personal handwriting recognition. ## Model Details - **Base Model:** microsoft/trocr-base-handwritten - **Training Data:** ~1,300 labeled handwritten lines from reMarkable tablet notes - **Performance:** CER 24%, WER 51% - **Training:** 15 epochs on Apple M4 Max ## Usage ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image processor = TrOCRProcessor.from_pretrained("moonstripe/chickenscratch-ocr") model = VisionEncoderDecoderModel.from_pretrained("moonstripe/chickenscratch-ocr") # Load a handwritten line image image = Image.open("line.png").convert("RGB") # Process and predict pixel_values = processor(images=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) ``` ## Training This model was trained using the [chickenscratch-ocr](https://github.com/moonstripe/chickenscratch-ocr) pipeline, which provides: - Data collection from reMarkable tablet - Web UI for labeling handwritten lines - Fine-tuning script with Apple Silicon MPS support ## Limitations - Optimized for a specific person's handwriting - Best results on single text lines (not full pages) - May struggle with unusual symbols or very messy writing
Source context: 8 downloads · 1 likes · Pipeline image-to-text · Repo moonstripe/chickenscratch-ocr
