docs/examples_en.md

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Examples

Recognize PDF Files and Return Markdown Format

For PDF files, you can use the .recognize_pdf() function to recognize the entire file or specific pages and output the results as a Markdown file. For example, for the following PDF file (examples/test-doc.pdf), you can call the function like this:

from pix2text import Pix2Text

img_fp = './examples/test-doc.pdf'
p2t = Pix2Text.from_config()
doc = p2t.recognize_pdf(img_fp, page_numbers=[0, 1])
doc.to_markdown('output-md')  # The exported Markdown information is saved in the output-md directory

You can also achieve the same functionality using the command line. Below is a command that uses the premium models (MFD + MFR + CnOCR) for recognition:

p2t predict -l en,ch_sim --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --rec-kwargs '{"page_numbers": [0, 1]}' --resized-shape 768 --file-type pdf -i docs/examples/test-doc.pdf -o output-md --save-debug-res output-debug

The recognition result can be found in output-md/output.md.

If you wish to export formats other than Markdown, such as Word, HTML, PDF, etc., it is recommended to use the tool Pandoc to convert the Markdown result.

Recognize Images with Complex Layout

You can use the .recognize_page() function to recognize text and mathematical formulas in images. For example, for the following image (examples/page2.png):

{: style="width:600px"}

You can call the function like this:

from pix2text import Pix2Text

img_fp = './examples/test-doc.pdf'
p2t = Pix2Text.from_config()
page = p2t.recognize_page(img_fp)
page.to_markdown('output-page')  # The exported Markdown information is saved in the output-page directory

You can also achieve the same functionality using the command line. Below is a command that uses the premium models (MFD + MFR + CnOCR) for recognition:

p2t predict -l en,ch_sim --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --resized-shape 768 --file-type page -i docs/examples/page2.png -o output-page --save-debug-res output-debug-page

The recognition result is similar to output-md/output.md.

Recognize Paragraph Images with Both Formulas and Texts

For paragraph images containing both formulas and texts, you don't need to use the layout analysis model. You can use the .recognize_text_formula() function to recognize both texts and mathematical formulas in the image. For example, for the following image (examples/en1.jpg):

{: style="width:600px"}

You can call the function like this:

from pix2text import Pix2Text, merge_line_texts

img_fp = './examples/en1.jpg'
p2t = Pix2Text.from_config()
outs = p2t.recognize_text_formula(img_fp, resized_shape=768, return_text=True)
print(outs)

The returned result outs is a dictionary, where the key position represents the box position information, type represents the category information, and text represents the recognition result. For detailed explanations, see API Documentation.

You can also achieve the same functionality using the command line. Below is a command that uses the premium models (MFD + MFR + CnOCR) for recognition:

p2t predict -l en,ch_sim --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --resized-shape 768 --file-type text_formula -i docs/examples/en1.jpg --save-debug-res out-debug-en1.jpg

Or use the free open-source models for recognition:

p2t predict -l en,ch_sim --resized-shape 768 --file-type text_formula -i docs/examples/en1.jpg --save-debug-res out-debug-en1.jpg

Recognize Pure Formula Images

For images containing only mathematical formulas, you can use the .recognize_formula() function to recognize the formulas as LaTeX expressions. For example, for the following image (examples/math-formula-42.png):

{: style="width:300px"}

You can call the function like this:

from pix2text import Pix2Text

img_fp = './examples/math-formula-42.png'
p2t = Pix2Text.from_config()
outs = p2t.recognize_formula(img_fp)
print(outs)

The returned result is a string representing the corresponding LaTeX expression. For detailed explanations, see Usage.

You can also achieve the same functionality using the command line. Below is a command that uses the premium model (MFR) for recognition:

p2t predict -l en,ch_sim --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --file-type formula -i docs/examples/math-formula-42.png

Or use the free open-source model for recognition:

p2t predict -l en,ch_sim --file-type formula -i docs/examples/math-formula-42.png

Recognize Pure Text Images

For images containing only text without mathematical formulas, you can use the .recognize_text() function to recognize the text in the image. In this case, Pix2Text acts as a general text OCR engine. For example, for the following image (examples/general.jpg):

{: style="width:400px"}

You can call the function like this:

from pix2text import Pix2Text

img_fp = './examples/general.jpg'
p2t = Pix2Text.from_config()
outs = p2t.recognize_text(img_fp)
print(outs)

The returned result is a string representing the corresponding text sequence. For detailed explanations, see API Documentation.

You can also achieve the same functionality using the command line. Below is a command that uses the premium model (CnOCR) for recognition:

p2t predict -l en,ch_sim --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --file-type text --no-return-text -i docs/examples/general.jpg --save-debug-res out-debug-general.jpg

Or use the free open-source model for recognition:

p2t predict -l en,ch_sim --file-type text --no-return-text -i docs/examples/general.jpg --save-debug-res out-debug-general.jpg

For Different Languages

English

Recognition Result:

Recognition Command:

p2t predict -l en --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --resized-shape 768 --file-type text_formula -i docs/examples/en1.jpg

Simplified Chinese

Recognition Result:

Recognition Command:

p2t predict -l en,ch_sim --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --text-ocr-config '{"rec_model_name": "doc-densenet_lite_666-gru_large"}' --resized-shape 768 --auto-line-break --file-type text_formula -i docs/examples/mixed.jpg --save-debug-res out-debug-mixed.jpg

Traditional Chinese

Recognition Result:

Recognition Command:

p2t predict -l en,ch_tra --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --resized-shape 768 --auto-line-break --file-type text_formula -i docs/examples/ch_tra.jpg --save-debug-res out-debug-tra.jpg

Note ⚠️: Please install the multilingual version of pix2text using the following command:

pip install pix2text[multilingual]

Vietnamese

Recognition Result:

Recognition Command:

p2t predict -l en,vi --mfd-config '{"model_name": "mfd-pro-1.5", "model_backend": "onnx"}' --formula-ocr-config '{"model_name":"mfr-pro-1.5","model_backend":"onnx"}' --resized-shape 608 --no-auto-line-break --file-type text_formula -i docs/examples/vietnamese.jpg --save-debug-res out-debug-vi.jpg

Note ⚠️: Please install the multilingual version of pix2text using the following command:

pip install pix2text[multilingual]