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<h1>API Reference</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#image-to-latex-conversion">Image to LaTeX Conversion</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.img2latex"><code class="docutils literal notranslate"><span class="pre">img2latex()</span></code></a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#paragraph-to-markdown-conversion">Paragraph to Markdown Conversion</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.paragraph2md"><code class="docutils literal notranslate"><span class="pre">paragraph2md()</span></code></a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_tokenizer"><code class="docutils literal notranslate"><span class="pre">load_tokenizer()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_latexdet_model"><code class="docutils literal notranslate"><span class="pre">load_latexdet_model()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_textdet_model"><code class="docutils literal notranslate"><span class="pre">load_textdet_model()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_textrec_model"><code class="docutils literal notranslate"><span class="pre">load_textrec_model()</span></code></a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.to_katex"><code class="docutils literal notranslate"><span class="pre">to_katex()</span></code></a></li>
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<section id="api-reference">
<h1>API Reference<a class="headerlink" href="#api-reference" title="Link to this heading">#</a></h1>
<p>This section provides detailed API documentation for the TexTeller package. TexTeller is a tool for detecting and recognizing LaTeX formulas in images and converting mixed text and formula images to markdown.</p>
<nav class="contents local" id="table-of-contents">
<p class="topic-title">Table of Contents</p>
<ul class="simple">
<li><p><a class="reference internal" href="#image-to-latex-conversion" id="id1">Image to LaTeX Conversion</a></p></li>
<li><p><a class="reference internal" href="#paragraph-to-markdown-conversion" id="id2">Paragraph to Markdown Conversion</a></p></li>
<li><p><a class="reference internal" href="#latex-detection" id="id3">LaTeX Detection</a></p></li>
<li><p><a class="reference internal" href="#model-loading" id="id4">Model Loading</a></p></li>
<li><p><a class="reference internal" href="#katex-conversion" id="id5">KaTeX Conversion</a></p></li>
</ul>
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<section id="image-to-latex-conversion">
<h2><a class="toc-backref" href="#id1" role="doc-backlink">Image to LaTeX Conversion</a><a class="headerlink" href="#image-to-latex-conversion" title="Link to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.img2latex">
<span class="sig-name descname"><span class="pre">img2latex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">VisionEncoderDecoderModel</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">ORTModelForVision2Seq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">RobertaTokenizerFast</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">images</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.8)"><span class="pre">device</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_format</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'latex'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'katex'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'latex'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_style</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_tokens</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_beams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_repeat_ngram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/texteller/api/inference.html#img2latex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.img2latex" title="Link to this definition">#</a></dt>
<dd><p>Convert images to LaTeX or KaTeX formatted strings.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – The TexTeller or ORTModelForVision2Seq model instance</p></li>
<li><p><strong>tokenizer</strong> – The tokenizer for the model</p></li>
<li><p><strong>images</strong> – List of image paths or numpy arrays (RGB format)</p></li>
<li><p><strong>device</strong> – The torch device to use (defaults to available GPU or CPU)</p></li>
<li><p><strong>out_format</strong> – Output format, either “latex” or “katex”</p></li>
<li><p><strong>keep_style</strong> – Whether to keep the style of the LaTeX</p></li>
<li><p><strong>max_tokens</strong> – Maximum number of tokens to generate</p></li>
<li><p><strong>num_beams</strong> – Number of beams for beam search</p></li>
<li><p><strong>no_repeat_ngram_size</strong> – Size of n-grams to prevent repetition</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>List of LaTeX or KaTeX strings corresponding to each input image</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_model</span><span class="p">,</span> <span class="n">load_tokenizer</span><span class="p">,</span> <span class="n">img2latex</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">model_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_onnx</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_tokenizer</span><span class="p">(</span><span class="n">tokenizer_path</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">img2latex</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;path/to/image.png&quot;</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">out_format</span><span class="o">=</span><span class="s2">&quot;katex&quot;</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</section>
<section id="paragraph-to-markdown-conversion">
<h2><a class="toc-backref" href="#id2" role="doc-backlink">Paragraph to Markdown Conversion</a><a class="headerlink" href="#paragraph-to-markdown-conversion" title="Link to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.paragraph2md">
<span class="sig-name descname"><span class="pre">paragraph2md</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">latexdet_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">InferenceSession</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textdet_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">TextDetector</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textrec_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">TextRecognizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latexrec_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">VisionEncoderDecoderModel</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">ORTModelForVision2Seq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">RobertaTokenizerFast</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.8)"><span class="pre">device</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_beams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span><a class="reference internal" href="_modules/texteller/api/inference.html#paragraph2md"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.paragraph2md" title="Link to this definition">#</a></dt>
<dd><p>Convert an image containing both text and mathematical formulas to markdown format.</p>
<p>This function processes a mixed-content image by:
1. Detecting mathematical formulas using a latex detection model
2. Masking detected formula areas and detecting text regions using OCR
3. Recognizing text in the detected regions
4. Converting formula regions to LaTeX using the latex recognition model
5. Combining all detected elements into a properly formatted markdown string</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>img_path</strong> – Path to the input image containing text and formulas</p></li>
<li><p><strong>latexdet_model</strong> – ONNX InferenceSession for LaTeX formula detection</p></li>
<li><p><strong>textdet_model</strong> – OCR text detector model</p></li>
<li><p><strong>textrec_model</strong> – OCR text recognition model</p></li>
<li><p><strong>latexrec_model</strong> – TexTeller model for LaTeX formula recognition</p></li>
<li><p><strong>tokenizer</strong> – Tokenizer for the LaTeX recognition model</p></li>
<li><p><strong>device</strong> – The torch device to use (defaults to available GPU or CPU)</p></li>
<li><p><strong>num_beams</strong> – Number of beams for beam search during LaTeX generation</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Markdown formatted string containing the recognized text and formulas</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_latexdet_model</span><span class="p">,</span> <span class="n">load_textdet_model</span><span class="p">,</span> <span class="n">load_textrec_model</span><span class="p">,</span> <span class="n">load_tokenizer</span><span class="p">,</span> <span class="n">paragraph2md</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Load all required models</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">latexdet_model</span> <span class="o">=</span> <span class="n">load_latexdet_model</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">textdet_model</span> <span class="o">=</span> <span class="n">load_textdet_model</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">textrec_model</span> <span class="o">=</span> <span class="n">load_textrec_model</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">latexrec_model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_tokenizer</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Convert image to markdown</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">markdown_text</span> <span class="o">=</span> <span class="n">paragraph2md</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">img_path</span><span class="o">=</span><span class="s2">&quot;path/to/mixed_content_image.jpg&quot;</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">latexdet_model</span><span class="o">=</span><span class="n">latexdet_model</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">textdet_model</span><span class="o">=</span><span class="n">textdet_model</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">textrec_model</span><span class="o">=</span><span class="n">textrec_model</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">latexrec_model</span><span class="o">=</span><span class="n">latexrec_model</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</section>
<section id="latex-detection">
<h2><a class="toc-backref" href="#id3" role="doc-backlink">LaTeX Detection</a><a class="headerlink" href="#latex-detection" title="Link to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.detection.latex_detect">
<span class="sig-name descname"><span class="pre">latex_detect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">InferenceSession</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><span class="pre">Bbox</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="_modules/texteller/api/detection/detect.html#latex_detect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.detection.latex_detect" title="Link to this definition">#</a></dt>
<dd><p>Detect LaTeX formulas in an image and classify them as isolated or embedded.</p>
<p>This function uses an ONNX model to detect LaTeX formulas in images. The model
identifies two types of LaTeX formulas:
- ‘isolated’: Standalone LaTeX formulas (typically displayed equations)
- ‘embedding’: Inline LaTeX formulas embedded within text</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>img_path</strong> – Path to the input image file</p></li>
<li><p><strong>predictor</strong> – ONNX InferenceSession model for LaTeX detection</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>List of Bbox objects representing the detected LaTeX formulas with their
positions, classifications, and confidence scores</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller.api</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_latexdet_model</span><span class="p">,</span> <span class="n">latex_detect</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">load_latexdet_model</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">bboxes</span> <span class="o">=</span> <span class="n">latex_detect</span><span class="p">(</span><span class="s2">&quot;path/to/image.png&quot;</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</section>
<section id="model-loading">
<h2><a class="toc-backref" href="#id4" role="doc-backlink">Model Loading</a><a class="headerlink" href="#model-loading" title="Link to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.load_model">
<span class="sig-name descname"><span class="pre">load_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_onnx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">VisionEncoderDecoderModel</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">ORTModelForVision2Seq</span></span></span><a class="reference internal" href="_modules/texteller/api/load.html#load_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.load_model" title="Link to this definition">#</a></dt>
<dd><p>Load the TexTeller model for LaTeX recognition.</p>
<p>This function loads the main TexTeller model, which is responsible for
converting images to LaTeX. It can load either the standard PyTorch model
or the optimized ONNX version.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_dir</strong> – Directory containing the model files. If None, uses the default model.</p></li>
<li><p><strong>use_onnx</strong> – Whether to load the ONNX version of the model for faster inference.
Requires the ‘optimum’ package and ONNX Runtime.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Loaded TexTeller model instance</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_model</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">use_onnx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.load_tokenizer">
<span class="sig-name descname"><span class="pre">load_tokenizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenizer_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">RobertaTokenizerFast</span></span></span><a class="reference internal" href="_modules/texteller/api/load.html#load_tokenizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.load_tokenizer" title="Link to this definition">#</a></dt>
<dd><p>Load the tokenizer for the TexTeller model.</p>
<p>This function loads the tokenizer used by the TexTeller model for
encoding and decoding LaTeX sequences.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>tokenizer_dir</strong> – Directory containing the tokenizer files. If None, uses the default tokenizer.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>RobertaTokenizerFast instance</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tokenizer</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_tokenizer</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.load_latexdet_model">
<span class="sig-name descname"><span class="pre">load_latexdet_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">InferenceSession</span></span></span><a class="reference internal" href="_modules/texteller/api/load.html#load_latexdet_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.load_latexdet_model" title="Link to this definition">#</a></dt>
<dd><p>Load the LaTeX detection model.</p>
<p>This function loads the model responsible for detecting LaTeX formulas in images.
The model is implemented as an ONNX InferenceSession for optimal performance.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>ONNX InferenceSession for LaTeX detection</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_latexdet_model</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">detector</span> <span class="o">=</span> <span class="n">load_latexdet_model</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.load_textdet_model">
<span class="sig-name descname"><span class="pre">load_textdet_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TextDetector</span></span></span><a class="reference internal" href="_modules/texteller/api/load.html#load_textdet_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.load_textdet_model" title="Link to this definition">#</a></dt>
<dd><p>Load the text detection model.</p>
<p>This function loads the model responsible for detecting text regions in images.
It’s based on PaddleOCR’s text detection model.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>PaddleOCR TextDetector instance</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_textdet_model</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">text_detector</span> <span class="o">=</span> <span class="n">load_textdet_model</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.load_textrec_model">
<span class="sig-name descname"><span class="pre">load_textrec_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TextRecognizer</span></span></span><a class="reference internal" href="_modules/texteller/api/load.html#load_textrec_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.load_textrec_model" title="Link to this definition">#</a></dt>
<dd><p>Load the text recognition model.</p>
<p>This function loads the model responsible for recognizing regular text in images.
It’s based on PaddleOCR’s text recognition model.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>PaddleOCR TextRecognizer instance</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">texteller</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_textrec_model</span>
<span class="gp">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">text_recognizer</span> <span class="o">=</span> <span class="n">load_textrec_model</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
</section>
<section id="katex-conversion">
<h2><a class="toc-backref" href="#id5" role="doc-backlink">KaTeX Conversion</a><a class="headerlink" href="#katex-conversion" title="Link to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="texteller.api.to_katex">
<span class="sig-name descname"><span class="pre">to_katex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span><a class="reference internal" href="_modules/texteller/api/katex.html#to_katex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#texteller.api.to_katex" title="Link to this definition">#</a></dt>
<dd><p>Convert LaTeX formula to KaTeX-compatible format.</p>
<p>This function processes a LaTeX formula string and converts it to a format
that is compatible with KaTeX rendering. It removes unsupported commands
and structures, simplifies LaTeX environments, and optimizes the formula
for web display.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>formula</strong> – LaTeX formula string to convert</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>KaTeX-compatible formula string</p>
</dd>
</dl>
</dd></dl>
</section>
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<i class="fa-solid fa-list"></i> Contents
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#image-to-latex-conversion">Image to LaTeX Conversion</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.img2latex"><code class="docutils literal notranslate"><span class="pre">img2latex()</span></code></a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#paragraph-to-markdown-conversion">Paragraph to Markdown Conversion</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.paragraph2md"><code class="docutils literal notranslate"><span class="pre">paragraph2md()</span></code></a></li>
</ul>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#latex-detection">LaTeX Detection</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.detection.latex_detect"><code class="docutils literal notranslate"><span class="pre">latex_detect()</span></code></a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-loading">Model Loading</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_model"><code class="docutils literal notranslate"><span class="pre">load_model()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_tokenizer"><code class="docutils literal notranslate"><span class="pre">load_tokenizer()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_latexdet_model"><code class="docutils literal notranslate"><span class="pre">load_latexdet_model()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_textdet_model"><code class="docutils literal notranslate"><span class="pre">load_textdet_model()</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.load_textrec_model"><code class="docutils literal notranslate"><span class="pre">load_textrec_model()</span></code></a></li>
</ul>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#katex-conversion">KaTeX Conversion</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#texteller.api.to_katex"><code class="docutils literal notranslate"><span class="pre">to_katex()</span></code></a></li>
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