PaddlePaddle/PP-FormulaNet-S
1136
1---2license: apache-2.03library_name: PaddleOCR4language:5- en6- zh7pipeline_tag: image-to-text8tags:9- OCR10- PaddlePaddle11- PaddleOCR12- formula_recognition13---14 15# PP-FormulaNet-S16 17## Introduction18 19PP-FormulaNet is an advanced formula recognition model developed by the PaddleOCR Team. The PP-FormulaNet includes multiple versions: L, and S, where PP-FormulaNet-S uses PP-HGNetV2-B4 as its backbone network. Through parallel masking and model distillation techniques, it significantly improves inference speed while maintaining high recognition accuracy, making it suitable for applications requiring fast inference. The key accuracy metrics are as follow:20 21 22| Model | Backbone | En-BLEU↑ |Zh-BLEU(%)↑ | GPU Inference Time (ms)| 23|:----------------:|:---------:|:-----------------:|:--------------:|:--------------:|24| UniMERNet | Donut Swin | 85.91 | 43.50 | 2266.96 | 25| <b>PP-FormulaNet-S</b> | <b>PPHGNetV2_B4</b> | <b>87.00</b> | <b>45.71</b> | <b>202.25</b> |26| PP-FormulaNet-L | Vary_VIT_B | 90.36 | 45.78 | 1976.52 |27| PP-FormulaNet_plus-S | PPHGNetV2_B4 | 88.71 | 53.32 | 191.69 |28| PP-FormulaNet_plus-M | PPHGNetV2_B6 | 91.45 | 89.76 | 1301.56 |29| PP-FormulaNet_plus-L | Vary_VIT_B | 92.22 | 90.64 | 1745.25 |30| LaTeX-OCR | Hybrid ViT | 74.55 | 39.96 | 1244.61 |31 32Note: En-BLEU and Zh-BLEU (%) represent the BLEU scores for English formulas and Chinese formulas, respectively. The evaluation dataset for English formulas includes simple and complex formulas from UniMERNet, as well as simple, intermediate, and complex formulas from PaddleX’s internally developed dataset. The evaluation dataset for Chinese formulas comes from PaddleX’s internally developed Chinese formula dataset.33 34## Quick Start35 36### Installation37 381. PaddlePaddle39 40Please refer to the following commands to install PaddlePaddle using pip:41 42```bash43# for CUDA11.844python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/45 46# for CUDA12.647python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/48 49# for CPU50python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/51```52 53For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick).54 552. PaddleOCR56 57Install the latest version of the PaddleOCR inference package from PyPI:58 59```bash60python -m pip install paddleocr61```62 63### Model Usage64 65You can quickly experience the functionality with a single command:66 67```bash68paddleocr formula_recognition \69 --model_name PP-FormulaNet-S \70 -i https://cdn-uploads.huggingface.co/production/uploads/68493f0616e67d38f02f138a/4kkIUGxXMGozIg6U1BIxZ.png71```72 73You can also integrate the model inference of the formula recognition module into your project. Before running the following code, please download the [sample image](https://cdn-uploads.huggingface.co/production/uploads/68493f0616e67d38f02f138a/4kkIUGxXMGozIg6U1BIxZ.png) to your local machine.74 75```python76from paddleocr import FormulaRecognition77model = FormulaRecognition(model_name="PP-FormulaNet-S")78output = model.predict(input="4kkIUGxXMGozIg6U1BIxZ.png", batch_size=1)79for res in output:80 res.print()81 res.save_to_img(save_path="./output/")82 res.save_to_json(save_path="./output/res.json")83```84 85After running, the obtained result is as follows:86 87```json88{'res': {'input_path': '4kkIUGxXMGozIg6U1BIxZ.png', 'page_index': None, 'rec_formula': '\\zeta_{0}(\\nu)=-{\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z{\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}}\\ \\ {vec\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'}}89```90<b>Note: If you need to visualize the formula recognition module, you must install the LaTeX rendering environment by running the following command. Currently, visualization is only supported on Ubuntu. Other environments are not supported for now. For complex formulas, the LaTeX result may contain advanced representations that may not render successfully in Markdown or similar environments:</b>91```bash92sudo apt-get update93sudo apt-get install texlive texlive-latex-base texlive-xetex latex-cjk-all texlive-latex-extra -y94```95 96The visualized image is as follows:97 9899 100For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/formula_recognition.html#iii-quick-start).101 102### Pipeline Usage103 104The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.105 106#### Formula Recognition Pipeline107 108The formula recognition pipeline is designed to solve formula recognition tasks by extracting formula information from images and outputting it in LaTeX source code format. And there are 4 modules in the pipeline: 109* Document Image Orientation Classification Module (Optional)110* Text Image Unwarping Module (Optional)111* Layout Detection Module (Optional)112* Formula Recognition Module113 114Run a single command to quickly experience the Formula Recognition Pipeline. Before running the code below, please download the [example image](https://cdn-uploads.huggingface.co/production/uploads/68493f0616e67d38f02f138a/4HrLNUf2yKGI8CwN9axpt.png) locally:115 116```bash117paddleocr formula_recognition_pipeline -i https://cdn-uploads.huggingface.co/production/uploads/68493f0616e67d38f02f138a/4HrLNUf2yKGI8CwN9axpt.png \118 --formula_recognition_model_name PP-FormulaNet-S \119 --save_path ./output \120 --device gpu:0 121```122 123Results are printed to the terminal:124 125```json126{'res': {'input_path': '/root/.paddlex/predict_input/4HrLNUf2yKGI8CwN9axpt.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_layout_detection': True}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': True}, 'angle': 0}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 2, 'label': 'text', 'score': 0.9855162501335144, 'coordinate': [90.5582, 1086.7775, 658.8992, 1553.267]}, {'cls_id': 2, 'label': 'text', 'score': 0.9814791679382324, 'coordinate': [93.042145, 127.992386, 664.8606, 396.60297]}, {'cls_id': 2, 'label': 'text', 'score': 0.9767233729362488, 'coordinate': [698.44617, 591.048, 1293.3668, 748.28625]}, {'cls_id': 2, 'label': 'text', 'score': 0.9712724089622498, 'coordinate': [701.4879, 286.62286, 1299.0151, 391.8841]}, {'cls_id': 2, 'label': 'text', 'score': 0.9708836078643799, 'coordinate': [697.0071, 751.9401, 1290.2227, 883.6447]}, {'cls_id': 2, 'label': 'text', 'score': 0.9688520431518555, 'coordinate': [704.01917, 79.636734, 1304.7367, 187.96138]}, {'cls_id': 2, 'label': 'text', 'score': 0.9683284163475037, 'coordinate': [93.07703, 799.36597, 660.6864, 902.0364]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9660061597824097, 'coordinate': [728.5604, 440.9317, 1224.097, 570.8568]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9615049958229065, 'coordinate': [723.025, 1333.5005, 1257.1569, 1468.0688]}, {'cls_id': 7, 'label': 'formula', 'score': 0.961004376411438, 'coordinate': [777.5282, 207.88376, 1222.9387, 267.32993]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9609803557395935, 'coordinate': [756.4403, 1211.3208, 1188.0408, 1268.2334]}, {'cls_id': 2, 'label': 'text', 'score': 0.959402322769165, 'coordinate': [697.5221, 957.6737, 1288.6223, 1033.5424]}, {'cls_id': 2, 'label': 'text', 'score': 0.9592350125312805, 'coordinate': [691.3296, 1511.7983, 1282.0968, 1642.5952]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9590734839439392, 'coordinate': [153.89197, 924.2169, 601.09546, 1036.9056]}, {'cls_id': 2, 'label': 'text', 'score': 0.9582054615020752, 'coordinate': [87.024506, 1557.2972, 655.9558, 1632.701]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9579665064811707, 'coordinate': [810.86975, 1057.1064, 1175.1078, 1117.6572]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9557791352272034, 'coordinate': [165.2392, 557.844, 598.2451, 614.3662]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9539064764976501, 'coordinate': [116.46484, 713.8796, 614.2107, 774.029]}, {'cls_id': 2, 'label': 'text', 'score': 0.9520670175552368, 'coordinate': [96.68561, 478.32416, 662.5837, 536.60223]}, {'cls_id': 2, 'label': 'text', 'score': 0.9442729949951172, 'coordinate': [96.14572, 639.16113, 661.80334, 692.4822]}, {'cls_id': 2, 'label': 'text', 'score': 0.940317690372467, 'coordinate': [695.9426, 1138.6841, 1286.7327, 1188.0151]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9249900579452515, 'coordinate': [852.94556, 908.64923, 1131.185, 933.8394]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9248911142349243, 'coordinate': [195.27357, 424.8133, 567.68335, 451.1208]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.9173402786254883, 'coordinate': [1246.2461, 1079.063, 1286.333, 1104.3276]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.9168799519538879, 'coordinate': [1246.8928, 908.664, 1288.1958, 934.6163]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.915979266166687, 'coordinate': [1247.0377, 1229.1577, 1287.0939, 1254.9792]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.9086456894874573, 'coordinate': [1252.8517, 492.109, 1294.6124, 518.47156]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.9016352891921997, 'coordinate': [1242.1753, 1473.7004, 1283.019, 1498.6516]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.9000396728515625, 'coordinate': [1269.8044, 220.35562, 1299.8611, 247.01315]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8966289758682251, 'coordinate': [95.999916, 235.48334, 295.44852, 265.59302]}, {'cls_id': 2, 'label': 'text', 'score': 0.8954761028289795, 'coordinate': [696.8688, 1286.2268, 1083.3927, 1310.8733]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8951668739318848, 'coordinate': [166.62683, 129.18127, 511.6576, 156.2976]}, {'cls_id': 2, 'label': 'text', 'score': 0.8934891819953918, 'coordinate': [725.67053, 396.18787, 1263.0408, 422.78894]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.892305314540863, 'coordinate': [634.14246, 427.77844, 661.17773, 454.10535]}, {'cls_id': 2, 'label': 'text', 'score': 0.8891267776489258, 'coordinate': [94.483185, 1058.7578, 441.93515, 1082.4932]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.8877044320106506, 'coordinate': [630.4214, 939.2977, 657.7117, 965.36]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.8832477927207947, 'coordinate': [630.59216, 1000.9552, 657.4265, 1026.2094]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.8769293427467346, 'coordinate': [634.1151, 575.3828, 660.59314, 601.1638]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8733161091804504, 'coordinate': [95.2839, 1320.377, 264.92313, 1345.8511]}, {'cls_id': 17, 'label': 'formula_number', 'score': 0.8703839182853699, 'coordinate': [633.8277, 730.3137, 659.84564, 755.55347]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8392633199691772, 'coordinate': [365.18652, 268.2967, 515.7993, 296.06952]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8316442370414734, 'coordinate': [1090.5394, 1599.1625, 1276.672, 1622.1669]}, {'cls_id': 7, 'label': 'formula', 'score': 0.817266583442688, 'coordinate': [246.17564, 161.22665, 314.3683, 186.41296]}, {'cls_id': 3, 'label': 'number', 'score': 0.8043113350868225, 'coordinate': [1297.4021, 7.143423, 1310.6084, 27.74441]}, {'cls_id': 7, 'label': 'formula', 'score': 0.797702968120575, 'coordinate': [538.4617, 478.0895, 661.8972, 508.51443]}, {'cls_id': 7, 'label': 'formula', 'score': 0.7646714448928833, 'coordinate': [916.5115, 1618.5238, 1009.6382, 1640.8141]}, {'cls_id': 7, 'label': 'formula', 'score': 0.7432729005813599, 'coordinate': [694.83765, 1612.2528, 861.0532, 1635.9652]}, {'cls_id': 7, 'label': 'formula', 'score': 0.7072135806083679, 'coordinate': [99.70873, 508.2096, 254.92291, 535.7488]}, {'cls_id': 7, 'label': 'formula', 'score': 0.6994448304176331, 'coordinate': [696.79846, 1561.4436, 899.79236, 1586.7415]}, {'cls_id': 7, 'label': 'formula', 'score': 0.6704866886138916, 'coordinate': [1117.0674, 1572.0345, 1191.5293, 1594.7426]}, {'cls_id': 7, 'label': 'formula', 'score': 0.6333382725715637, 'coordinate': [577.34186, 1274.4236, 602.5528, 1296.696]}, {'cls_id': 7, 'label': 'formula', 'score': 0.621163010597229, 'coordinate': [175.29213, 349.82397, 241.25047, 376.66553]}, {'cls_id': 7, 'label': 'formula', 'score': 0.6146791577339172, 'coordinate': [773.0735, 595.1659, 800.43823, 617.38635]}, {'cls_id': 7, 'label': 'formula', 'score': 0.6107904314994812, 'coordinate': [706.67114, 316.8644, 736.7178, 339.93387]}, {'cls_id': 7, 'label': 'formula', 'score': 0.5521712899208069, 'coordinate': [1263.9779, 314.65396, 1292.8451, 337.40207]}, {'cls_id': 7, 'label': 'formula', 'score': 0.5341379046440125, 'coordinate': [1219.2937, 316.60284, 1243.9319, 339.71375]}, {'cls_id': 7, 'label': 'formula', 'score': 0.520746111869812, 'coordinate': [254.65915, 323.65292, 326.58456, 349.53452]}, {'cls_id': 7, 'label': 'formula', 'score': 0.5011299848556519, 'coordinate': [255.84404, 1350.6619, 301.73444, 1375.5315]}]}, 'formula_res_list': [{'rec_formula': '\\begin{aligned}{\\psi_{0}(M)-\\psi(M,z)=}&{{}\\frac{(1-\\epsilon_{r})}{\\epsilon_{r}}\\frac{\\lambda^{2}c^{2}}{t_{\\operatorname{E}}^{2}\\operatorname{l n}(10)}\\times}\\\\ {}&{{}\\int_{0}^{z}d z^{\\prime}\\frac{d t}{d z^{\\prime}}\\left.\\frac{\\partial\\phi}{\\partial L}\\right|_{L=\\lambda M c^{2}/t_{\\operatorname{E}}},}\\\\ \\end{aligned}', 'formula_region_id': 1, 'dt_polys': ([728.5604, 440.9317, 1224.097, 570.8568],)}, {'rec_formula': '\\begin{aligned}{p(\\operatorname{l o g}_{10}}&{{}M|\\operatorname{l o g}_{10}\\sigma)=\\frac{1}{\\sqrt{2\\pi}\\epsilon_{0}}}\\\\ {}&{{}\\times\\operatorname{e x p}\\left[-\\frac{1}{2}\\left(\\frac{\\operatorname{l o g}_{10}M-a_{\\bullet}-b_{\\bullet}\\operatorname{l o g}_{10}\\sigma}{\\epsilon_{0}}\\right)^{2}\\right]\\ .}}\\\\\\endaligned}}\\', 'formula_region_id': 2, 'dt_polys': ([723.025, 1333.5005, 1257.1569, 1468.0688],)}, {'rec_formula': '\\phi(L)\\equiv\\frac{d n}{d\\operatorname{l o g}_{10}L}=\\frac{\\phi_{*}}{(L/L_{*})^{\\gamma_{1}}+(L/L_{*})^{\\gamma_{2}}}.', 'formula_region_id': 3, 'dt_polys': ([777.5282, 207.88376, 1222.9387, 267.32993],)}, {'rec_formula': '\\psi_{0}(M)=\\int d\\sigma\\frac{p(\\log_{10}M|\\log_{10}\\sigma)}{M\\log(10)}\\frac{d n}{d\\sigma}(\\sigma),', 'formula_region_id': 4, 'dt_polys': ([756.4403, 1211.3208, 1188.0408, 1268.2334],)}, {'rec_formula': '\\begin{aligned}{\\rho_{\\operatorname{B H}}}&{{}=\\int d M\\psi(M)M}\\\\ {}&{{}=\\frac{1-\\epsilon_{r}}{\\epsilon_{r}c^{2}}\\int_{0}^{\\infty}d z\\frac{d t}{d z}\\int d\\operatorname{l o g}_{10}L\\phi(L,z)L,}\\\\ \\end{aligned}', 'formula_region_id': 5, 'dt_polys': ([153.89197, 924.2169, 601.09546, 1036.9056],)}, {'rec_formula': '\\frac{d n}{d\\sigma}d\\sigma=\\psi_{*}\\left(\\frac{\\sigma}{\\sigma_{*}}\\right)^{\\alpha}\\frac{e^{-(\\sigma/\\sigma_{*})^{\\beta}}}{\\Gamma(\\alpha/\\beta)}\\beta\\frac{d\\sigma}{\\sigma}.', 'formula_region_id': 6, 'dt_polys': ([810.86975, 1057.1064, 1175.1078, 1117.6572],)}, {'rec_formula': '\\langle\\dot{M}(M,t)\\rangle\\psi(M,t)=\\frac{(1-\\epsilon_{r})}{\\epsilon_{r}c^{2}\\operatorname{l n}(10)}\\phi(L,t)\\frac{d L}{d M}.', 'formula_region_id': 7, 'dt_polys': ([165.2392, 557.844, 598.2451, 614.3662],)}, {'rec_formula': '\\frac{\\partial\\psi}{\\partial t}(M,t)+\\frac{(1-\\epsilon_{r})}{\\epsilon_{r}}\\frac{\\lambda^{2}c^{2}}{t_{\\operatorname{E}}^{2}\\operatorname{l n}(10)}\\left.\\frac{\\partial\\phi}{\\partial L}\\right|_{L=\\lambda M c^{2}/t_{\\operatorname{E}}}=0,', 'formula_region_id': 8, 'dt_polys': ([116.46484, 713.8796, 614.2107, 774.029],)}, {'rec_formula': '\\log_{10}M=a_{\\bullet}+b_{\\bullet}\\log_{10}X.', 'formula_region_id': 9, 'dt_polys': ([852.94556, 908.64923, 1131.185, 933.8394],)}, {'rec_formula': '\\phi(L,t)d\\log_{1\\alpha}L=\\delta(M,t)\\psi(M,t)d M.', 'formula_region_id': 10, 'dt_polys': ([195.27357, 424.8133, 567.68335, 451.1208],)}, {'rec_formula': '\\dot{M}\\:=\\:(1-\\epsilon_{r})\\dot{M}_{\\mathrm{a c c}}', 'formula_region_id': 11, 'dt_polys': ([95.999916, 235.48334, 295.44852, 265.59302],)}, {'rec_formula': 't_{E}\\,=\\,\\sigma_{T}c/4\\pi G m_{v}\\,=\\,4.5\\times10^{8}\\mathrm{y r}', 'formula_region_id': 12, 'dt_polys': ([166.62683, 129.18127, 511.6576, 156.2976],)}, {'rec_formula': 'M_{*}\\,=\\,L_{*}t_{E}/\\bar{\\lambda}c^{2}', 'formula_region_id': 13, 'dt_polys': ([95.2839, 1320.377, 264.92313, 1345.8511],)}, {'rec_formula': '\\phi(L,t)d\\operatorname{l o g}_{10}L', 'formula_region_id': 14, 'dt_polys': ([365.18652, 268.2967, 515.7993, 296.06952],)}, {'rec_formula': 'a_{\\bullet}\\,=\\,8.32\\pm0.05', 'formula_region_id': 15, 'dt_polys': ([1090.5394, 1599.1625, 1276.672, 1622.1669],)}, {'rec_formula': '\\epsilon_{r}\\dot{M}_{\\operatorname{a c c}}', 'formula_region_id': 16, 'dt_polys': ([246.17564, 161.22665, 314.3683, 186.41296],)}, {'rec_formula': '\\langle\\dot{M}(M,t)\\rangle=', 'formula_region_id': 17, 'dt_polys': ([538.4617, 478.0895, 661.8972, 508.51443],)}, {'rec_formula': '\\epsilon_{0}=0.38', 'formula_region_id': 18, 'dt_polys': ([916.5115, 1618.5238, 1009.6382, 1640.8141],)}, {'rec_formula': 'b_{\\bullet}=5.64\\pm\\dot{0.3}2', 'formula_region_id': 19, 'dt_polys': ([694.83765, 1612.2528, 861.0532, 1635.9652],)}, {'rec_formula': '\\delta(M,t)\\dot{M}(M,t)', 'formula_region_id': 20, 'dt_polys': ([99.70873, 508.2096, 254.92291, 535.7488],)}, {'rec_formula': 'X\\,=\\,\\sigma/200\\mathrm{k m\\ \\,\\ s^{-1}}', 'formula_region_id': 21, 'dt_polys': ([696.79846, 1561.4436, 899.79236, 1586.7415],)}, {'rec_formula': 'M-\\sigma', 'formula_region_id': 22, 'dt_polys': ([1117.0674, 1572.0345, 1191.5293, 1594.7426],)}, {'rec_formula': 'L_{*}', 'formula_region_id': 23, 'dt_polys': ([577.34186, 1274.4236, 602.5528, 1296.696],)}, {'rec_formula': '\\phi(L,t)', 'formula_region_id': 24, 'dt_polys': ([175.29213, 349.82397, 241.25047, 376.66553],)}, {'rec_formula': '\\psi_{0}', 'formula_region_id': 25, 'dt_polys': ([773.0735, 595.1659, 800.43823, 617.38635],)}, {'rec_formula': '\\ mathrm A{{}}^{}', 'formula_region_id': 26, 'dt_polys': ([706.67114, 316.8644, 736.7178, 339.93387],)}, {'rec_formula': 'L_{*}', 'formula_region_id': 27, 'dt_polys': ([1263.9779, 314.65396, 1292.8451, 337.40207],)}, {'rec_formula': '\\phi_{*}', 'formula_region_id': 28, 'dt_polys': ([1219.2937, 316.60284, 1243.9319, 339.71375],)}, {'rec_formula': '\\delta(M,t)', 'formula_region_id': 29, 'dt_polys': ([254.65915, 323.65292, 326.58456, 349.53452],)}, {'rec_formula': '\\phi(L)', 'formula_region_id': 30, 'dt_polys': ([255.84404, 1350.6619, 301.73444, 1375.5315],)}]}}127```128 129If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below:130 131132 133The command-line method is for quick experience. For project integration, also only a few codes are needed as well:134 135```python136from paddleocr import FormulaRecognitionPipeline137 138pipeline = FormulaRecognitionPipeline(formula_recognition_model_name="PP-FormulaNet-S")139output = pipeline.predict("./4HrLNUf2yKGI8CwN9axpt.png")140for res in output:141 res.print() ## Print the structured output of the prediction142 res.save_to_img(save_path="output") ## Save the formula visualization result of the current image.143 res.save_to_json(save_path="output") ## Save the structured JSON result of the current image144```145 146For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/formula_recognition.html#2-quick-start).147 148 149## Links150 151[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)152 153[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)154 155