multicentury-htr-model-small-onnx

Model description

Model Name: multicentury-htr-model-small-onnx

Model Version: 202509_small_onnx

Model Type: Transformer-based OCR (TrOCR)

Base Model: microsoft/trocr-small-handwritten

Purpose: Handwritten text recognition

Languages: Swedish, Finnish

License: Apache 2.0

The repository contains an ONNX version of the small multicentury htr model. You can find more information on the model here

How to Use the Model

You can use the model with onnxruntime-library for example by using the code example below. Note that the model expects the input images to be text lines.

from transformers import TrOCRProcessor, VisionEncoderDecoderConfig
from huggingface_hub import snapshot_download
from PIL import Image
import numpy as np
import onnxruntime
import os

def generate(decoder, encoder_outputs, batch_size, config, max_length = 128):
        """
        Generate text using autoregressive decoding
        with per-sequence early stopping.
        
        Args:
            decoder: TrOCR decoder
            encoder_hidden_states: Output from encoder
            batch_size: Number of 2images to process
            config: model config information
            max_length: maximum length of generated sequence (in tokens)
            
        Returns:
            Generated token IDs
        """
        decoder_input_ids = np.full((batch_size, 1), 
                                    config.decoder_start_token_id, 
                                    dtype=np.int64)
            
        # Track which sequences have finished
        finished = np.zeros(batch_size, dtype=bool)
            
        for step in range(max_length):
            # Run decoder
            decoder_outputs = decoder.run(
                None,
                {
                    "input_ids": decoder_input_ids,
                    "encoder_hidden_states": encoder_outputs
                }
            )[0]

            # Get next tokens
            next_token_logits = decoder_outputs[:, -1, :]
            next_tokens = np.argmax(next_token_logits, axis=-1)
                
            # Mark sequences that just generated EOS
            just_finished = (next_tokens == config.eos_token_id)
            finished = finished | just_finished
                
            # For already finished sequences, force PAD token
            next_tokens[finished] = config.pad_token_id
                
            # Append tokens
            next_tokens = next_tokens.reshape(-1, 1)
            decoder_input_ids = np.concatenate([decoder_input_ids, next_tokens], axis=1)
                
            # Stop when ALL sequences have finished
            if np.all(finished):
                break
            
        return decoder_input_ids

def predict_text(line_image, processor, encoder, decoder, config):
    """
    Predict text content from text line images.
    
    Args:
        line_image: Text line image file
        processor: TrOCRProcessor
        encoder: TrOCR encoder
        decoder: TrOCR decoder
        config: model config information
            
    Returns:
        Generated text
    """
    # Process image with TrOCR processor
    # Use 'pt' (PyTorch) then convert to numpy, as 'np' is not supported by fast processors
    pixel_values = processor(line_image, return_tensors="pt").pixel_values
    pixel_values = pixel_values.numpy()
    batch_size = pixel_values.shape[0]
    # Get output from the encoder
    encoder_outputs = encoder.run(None, {"pixel_values": pixel_values})[0]
    # Get decoder output
    generated_ids = generate(decoder, encoder_outputs, batch_size, config)
    # Decode tokens to text
    texts = processor.batch_decode(
                generated_ids, 
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False
    )
    return texts

REPOSITORY = "Kansallisarkisto/multicentury-htr-model-small-onnx"

# Download repository
repo_path = snapshot_download(
    repo_id=REPOSITORY
)

# Load model and processor
processor = TrOCRProcessor.from_pretrained(repo_path,
                                            use_fast=True,
                                            do_resize=True, 
                                            size={'height': 192,'width': 1024})

# Load model config
config = VisionEncoderDecoderConfig.from_pretrained(repo_path)

providers=['CUDAExecutionProvider', 'CPUExecutionProvider']

# Load model encoder
encoder = onnxruntime.InferenceSession(
                os.path.join(repo_path, "encoder_model.onnx"),
                providers=providers
            )

# Load model decoder
decoder = onnxruntime.InferenceSession(
                os.path.join(repo_path, "decoder_model.onnx"),
                providers=providers
            )

# Open an image of handwritten text
image = Image.open("path_to_image.jpg")

# Preprocess and predict
generated_text = predict_text(image, processor, encoder, decoder, config)

print(' '.join(generated_text))