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"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: transformers in /opt/miniconda3/lib/python3.13/site-packages (4.52.4)\n",
"Requirement already satisfied: filelock in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (3.18.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (0.33.0)\n",
"Requirement already satisfied: numpy>=1.17 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (2.3.0)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (6.0.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (2024.11.6)\n",
"Requirement already satisfied: requests in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (2.32.3)\n",
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (0.21.1)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (0.5.3)\n",
"Requirement already satisfied: tqdm>=4.27 in /opt/miniconda3/lib/python3.13/site-packages (from transformers) (4.67.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /opt/miniconda3/lib/python3.13/site-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (2025.5.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/miniconda3/lib/python3.13/site-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (4.12.2)\n",
"Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /opt/miniconda3/lib/python3.13/site-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (1.1.4)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/miniconda3/lib/python3.13/site-packages (from requests->transformers) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/miniconda3/lib/python3.13/site-packages (from requests->transformers) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/miniconda3/lib/python3.13/site-packages (from requests->transformers) (2.3.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/miniconda3/lib/python3.13/site-packages (from requests->transformers) (2025.4.26)\n",
"Requirement already satisfied: torch in /opt/miniconda3/lib/python3.13/site-packages (2.7.1)\n",
"Requirement already satisfied: torchvision in /opt/miniconda3/lib/python3.13/site-packages (0.22.1)\n",
"Requirement already satisfied: torchaudio in /opt/miniconda3/lib/python3.13/site-packages (2.7.1)\n",
"Requirement already satisfied: filelock in /opt/miniconda3/lib/python3.13/site-packages (from torch) (3.18.0)\n",
"Requirement already satisfied: typing-extensions>=4.10.0 in /opt/miniconda3/lib/python3.13/site-packages (from torch) (4.12.2)\n",
"Requirement already satisfied: setuptools in /opt/miniconda3/lib/python3.13/site-packages (from torch) (78.1.1)\n",
"Requirement already satisfied: sympy>=1.13.3 in /opt/miniconda3/lib/python3.13/site-packages (from torch) (1.14.0)\n",
"Requirement already satisfied: networkx in /opt/miniconda3/lib/python3.13/site-packages (from torch) (3.5)\n",
"Requirement already satisfied: jinja2 in /opt/miniconda3/lib/python3.13/site-packages (from torch) (3.1.6)\n",
"Requirement already satisfied: fsspec in /opt/miniconda3/lib/python3.13/site-packages (from torch) (2025.5.1)\n",
"Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.13/site-packages (from torchvision) (2.3.0)\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/miniconda3/lib/python3.13/site-packages (from torchvision) (11.2.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/miniconda3/lib/python3.13/site-packages (from sympy>=1.13.3->torch) (1.3.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/miniconda3/lib/python3.13/site-packages (from jinja2->torch) (3.0.2)\n",
"Requirement already satisfied: Pillow in /opt/miniconda3/lib/python3.13/site-packages (11.2.1)\n",
"Requirement already satisfied: matplotlib in /opt/miniconda3/lib/python3.13/site-packages (3.10.3)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (1.3.2)\n",
"Requirement already satisfied: cycler>=0.10 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (4.58.4)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (1.4.8)\n",
"Requirement already satisfied: numpy>=1.23 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (2.3.0)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (24.2)\n",
"Requirement already satisfied: pillow>=8 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (11.2.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (3.2.3)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /opt/miniconda3/lib/python3.13/site-packages (from matplotlib) (2.9.0.post0)\n",
"Requirement already satisfied: six>=1.5 in /opt/miniconda3/lib/python3.13/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
]
}
],
"source": [
"#Run only at first\n",
"!pip install transformers\n",
"!pip install torch torchvision torchaudio\n",
"!pip install Pillow\n",
"!pip install matplotlib\n",
"#필수 라이브러리"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Core function to convert image to text using mathOCR\n",
"import torch\n",
"import time\n",
"from PIL import Image as PilImage, ImageOps # Renamed Image to PilImage to avoid conflict\n",
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel # Note: This still uses TrOCRProcessor/VisionEncoderDecoderModel from Hugging Face\n",
"\n",
"def run_mathOCR(image,\n",
" processor,\n",
" model,\n",
" measure_time: bool = False):\n",
" \"\"\"\n",
" Converts an image to text using mathOCR.\n",
"\n",
" Args:\n",
" image (PilImage.Image): Image to be OCR'd.\n",
" processor (TrOCRProcessor): Hugging Face TrOCR Processor (used for mathOCR).\n",
" model (VisionEncoderDecoderModel): Hugging Face TrOCR Model (used for mathOCR).\n",
" measure_time (bool): If True, prints execution time.\n",
" \"\"\"\n",
" if measure_time:\n",
" t0 = time.perf_counter()\n",
" \n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" model.to(device)\n",
"\n",
" # Process image to pixel values and move to device\n",
" pixel_values = processor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
"\n",
" # Generate text IDs\n",
" generated_ids = model.generate(\n",
" pixel_values,\n",
" max_new_tokens=256,\n",
" num_beams=4,\n",
" early_stopping=True\n",
" )\n",
"\n",
" # Decode generated IDs to text\n",
" generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
"\n",
" if measure_time:\n",
" t1 = time.perf_counter()\n",
" print(f\"[Timer] Elapsed: {t1 - t0:0.4f} sec\")\n",
"\n",
" return generated_text"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Function to preprocess the image\n",
"def preprocess_image(image_path, target_size=(384, 384)):\n",
" img = PilImage.open(image_path).convert(\"RGB\")\n",
" img = ImageOps.exif_transpose(img)\n",
" img = ImageOps.pad(img, target_size, color=\"white\")\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Config of the encoder: <class 'transformers.models.vit.modeling_vit.ViTModel'> is overwritten by shared encoder config: ViTConfig {\n",
" \"attention_probs_dropout_prob\": 0.0,\n",
" \"encoder_stride\": 16,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.0,\n",
" \"hidden_size\": 1024,\n",
" \"image_size\": 384,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 4096,\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"model_type\": \"vit\",\n",
" \"num_attention_heads\": 16,\n",
" \"num_channels\": 3,\n",
" \"num_hidden_layers\": 24,\n",
" \"patch_size\": 16,\n",
" \"pooler_act\": \"tanh\",\n",
" \"pooler_output_size\": 1024,\n",
" \"qkv_bias\": false,\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.51.3\"\n",
"}\n",
"\n",
"Config of the decoder: <class 'transformers.models.trocr.modeling_trocr.TrOCRForCausalLM'> is overwritten by shared decoder config: TrOCRConfig {\n",
" \"activation_dropout\": 0.0,\n",
" \"activation_function\": \"relu\",\n",
" \"add_cross_attention\": true,\n",
" \"attention_dropout\": 0.0,\n",
" \"bos_token_id\": 0,\n",
" \"classifier_dropout\": 0.0,\n",
" \"d_model\": 1024,\n",
" \"decoder_attention_heads\": 16,\n",
" \"decoder_ffn_dim\": 4096,\n",
" \"decoder_layerdrop\": 0.0,\n",
" \"decoder_layers\": 12,\n",
" \"decoder_start_token_id\": 2,\n",
" \"dropout\": 0.1,\n",
" \"encoder_hidden_size\": 1024,\n",
" \"eos_token_id\": 2,\n",
" \"init_std\": 0.02,\n",
" \"is_decoder\": true,\n",
" \"layernorm_embedding\": false,\n",
" \"max_position_embeddings\": 1024,\n",
" \"model_type\": \"trocr\",\n",
" \"pad_token_id\": 1,\n",
" \"scale_embedding\": true,\n",
" \"tie_word_embeddings\": false,\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.51.3\",\n",
" \"use_cache\": false,\n",
" \"use_learned_position_embeddings\": false,\n",
" \"vocab_size\": 50265\n",
"}\n",
"\n"
]
}
],
"source": [
"# --- Main execution flow ---\n",
"\n",
"# Load mathOCR model and processor once\n",
"model_name = \"fhswf/TrOCR_Math_handwritten\" # This is the specific model ID for handwritten math\n",
"processor = TrOCRProcessor.from_pretrained(model_name)\n",
"model = VisionEncoderDecoderModel.from_pretrained(model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Timer] Elapsed: 2.1033 sec\n",
"e^{i\\pi}+1=0.\n"
]
}
],
"source": [
"# Replace with your actual image path\n",
"image_path = \".jpg your image path\"# !!! IMPORTANT: Update this path !!!\n",
"img = preprocess_image(image_path)\n",
"\n",
"# Run mathOCR and print the result\n",
"extracted_text = run_mathOCR(img, processor, model, measure_time=True) #False if you don't want to display timer\n",
"print(extracted_text)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"image/png": {
"width": 200
}
},
"output_type": "display_data"
}
],
"source": [
"# Save and display LaTeX output as an image\n",
"from sympy import preview\n",
"from IPython.display import Image, display\n",
"import os\n",
"\n",
"latex_code = extracted_text\n",
"output_path = os.path.join(os.path.expanduser('~'), \"latex_output.png\")\n",
"\n",
"# Generate the PNG image from LaTeX code\n",
"preview(f\"${latex_code}$\", viewer='file', filename=output_path, euler=False)\n",
"\n",
"# Display the generated image in the output\n",
"display(Image(filename=output_path, width=200))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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