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This is a fine-tuned version of Microsoft's TrOCR-Large model, specifically trained to recognize handwritten chess notation from images of chess scoresheets. The model can accurately transcribe chess moves in...
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Description de la source
This is a fine-tuned version of Microsoft's TrOCR-Large model, specifically trained to recognize handwritten chess notation from images of chess scoresheets. The model can accurately transcribe chess moves in Standard Algebraic Notation (SAN) from handwritten images.
Vision Encoder-Decoder TrOCR (Transformer-based Optical Character Recognition)
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1 sourceVérifié 17 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
microsoft/trocr-large-handwrittenThe model was trained on a custom dataset of 13,731 handwritten chess move images with corresponding text labels in Standard Algebraic Notation.
Dataset Characteristics:
Images were resized to 384x384 pixels and converted to RGB format. Text was tokenized with chess-specific vocabulary and padded/truncated to a maximum length of 16 tokens.
| Metric | Value |
|---|---|
| Accuracy | ~92% |
| Character Error Rate (CER) | ~3% |
| Inference Speed | ~100 ms/image |
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
# Load model and processor
model = VisionEncoderDecoderModel.from_pretrained("username/trocr-chess-handwritten")
processor = TrOCRProcessor.from_pretrained("username/trocr-chess-handwritten")
# Load and process image
image = Image.open("chess_move.png").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values
# Generate prediction
generated_ids = model.generate(pixel_values)
predicted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"Predicted move: {predicted_text}")
--- language: en license: mit tags: - chess - ocr - handwritten-text-recognition - trocr - transformers - computer-vision - image-to-text datasets: - handwritten-chess-notation metrics: - accuracy - cer widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/chess_example.png example_title: Chess Move Example pipeline_tag: image-to-text --- # Handwritten Chess Notation Recognition with TrOCR ## Model Description This is a fine-tuned version of Microsoft's TrOCR-Large model, specifically trained to recognize handwritten chess notation from images of chess scoresheets. The model can accurately transcribe chess moves in Standard Algebraic Notation (SAN) from handwritten images. **Model Type:** Vision Encoder-Decoder **Architecture:** TrOCR (Transformer-based Optical Character Recognition) **Base Model:** `microsoft/trocr-large-handwritten` ## Intended Uses & Limitations ### Intended Use - Transcription of handwritten chess moves from scoresheet images - Digitization of historical chess games - Chess notation recognition in mobile apps - Educational tools for chess analysis ### Limitations - Works best with clear handwriting - Trained specifically on chess notation (not general text) - May struggle with extremely cursive handwriting - Requires well-lit, focused images ## Training Data The model was trained on a custom dataset of 13,731 handwritten chess move images with corresponding text labels in Standard Algebraic Notation. **Dataset Characteristics:** - Format: PNG images with move text annotations - Content: Chess moves in SAN format (e.g., "e4", "Nf3", "O-O", "Qxf7#") - Split: 88% training, 12% validation - Handwriting styles: Multiple variations ## Training Procedure ### Preprocessing Images were resized to 384x384 pixels and converted to RGB format. Text was tokenized with chess-specific vocabulary and padded/truncated to a maximum length of 16 tokens. ### Training Hyperparameters - **Epochs:** 6 - **Batch Size:** 1 (with gradient accumulation of 8) - **Learning Rate:** 3e-5 - **Optimizer:** AdamW - **Weight Decay:** 0.01 - **Warmup Steps:** 200 - **Mixed Precision:** FP16 ### Hardware - **GPU:** NVIDIA (8+ GB VRAM recommended) - **Training Time:** ~6 hours ## Evaluation Results | Metric | Value | |--------|-------| | Accuracy | ~92% | | Character Error Rate (CER) | ~3% | | Inference Speed | ~100 ms/image | #...
Source context: 100 downloads · 1 likes · Pipeline image-to-text · Library transformers · Repo uchihamadara1816/TROCR-Chess