We’re thrilled to announce that TextIn's PDF to Markdown plugin officially launched on the Coze platform! You can easily locate us by searching for "pdf2markdown" on Coze. Add us to your custom AI agents and start exploring our document parsing capability today.

Search for "pdf2markdown" on Coze, to find the plugin and easily integrate document parsing functionality into your custom AI agents.

If you would like to test the performance of document parsing plugin in advance, feel free to send your request to our chatbot. Believe me, our PDF to Markdown conversion will exceed your expectations.

Additionally, TextIn team had unveiled a simple workflow chat for your reference as below shown, please feel free to use.

[See what we can do]

Now, the "pdf to markdown" plugin offers Coze users the same high-quality service as TextIn's web interface and API calls, including:

• Large File Compatibility: We can handle files up to 500MB which is a soft limit that we plan to increase.
• Long Documents Compatibility: We support files contain up to 1000 pages which also plan to rise up to 5000 pages.
• Lightning Processing Speed: We can quickly parse a hundred-page PDF file in a snap.

We're giving users a 1,000-page quota of free trial as our way of saying a warm welcome!

PDF Processing ≠ Overwhelming

The launch of the "pdf to markdown" plugin provides a reliable tool of choice for users with PDF parsing needs.

Due to the nature of PDF files, the content is difficult to extract or edit. For a long time, PDFs have often been where knowledge goes to disappear. In the era of LLMs( Large Language Models), building "smart" AI requires not only computing power but also high-quality corpora. Hence, the shortage of language corpora has become a throny problem in the industry. Currently, a large amount of high-quality language data exists in books, papers, research reports, and corporate documents. While complex layout structures of above-mentioned mediums limit the processing of training corpora for LLMs(large language models) and the application of LLMs( Large Language Models) in Question-Answering over Documents (Docs-QA).

However, document parsing technology enables machines to recognize various elements in documents, and this is what TextIn can bring to the world by enhancing processing experience of recognizing multiple types of data such as text, tables, and images. Moreover, we empower computers to restore the reading order of documents, much like a human would, to better inspire and support the development of novel AI applications and intelligent agents.

TextIn’s document parsing technology leverages both physical and logical layout analysis to accurately identify and understand various elements within documents. Physical layout analysis deals with visual features and document structure. It aggregates related text into paragraphs or tables by leverage object detection techniques and regression-based single-stage detection models that enable computers to discern and analyze different layout patterns within the document. Moreover, logical layout analysis focuses on the semantic relationships between text blocks. It models these blocks based on their meaning to form structures like directory trees from hierarchical semantic relationships. Hence, by combining these methods, TextIn enhances document understanding and processing, and also making it easier to extract and interpret complex information.

TextIn has deep technical accumulation in the field of document intelligence with solid text and table recognition OCR technology and developing layout analysis capabilities. As we dive deeper into deep learning technology, TextIn's layout analysis capabilities have been significantly improved that make it possible to process complex document layouts. In addition, TextIn's layout analysis technology uses deep neural networks to automatically analyze and understand the layout and structure of document pages.

Layout analysis technology mainly includes the following key steps:

• Element detection: Using deep learning models such as object detection models (e.g., Faster R-CNN, YOLO, SSD), various elements in the document image are detected and located. These elements can include text, images, tables, titles, etc. Through element detection, the position and boarders of different elements in the document can be determined, providing a foundation for subsequent analysis and processing.
• Element classification: The next cool feature is that TextIn is capable to classify the detected elements by distinguishing between different types of elements such as text, images, tables, etc. This step can use image classification models or object classification models in deep learning to recognize and classify each element to be used for subsequent structure parsing and semantic understanding.
• Based on element detection and classification, structure parsing can identify the relationships and hierarchical structures between different elements within a document. This includes the correspondence between text paragraphs and titles as well as the relationships between different fields in tables. Hence, By analyzing the document layout and semantic information, deep learning models can automatically parse and understand the document's structure.
• Layout correction is applied to the detected document elements in order to ensure their positions and arrangements are more logical and consistent within the overall document. This process can involve operations such as text alignment, image correction, and table alignment to enhance the document’s readability and aesthetics.

Currently, the "PDF to Markdown" Coze plugin is equiped with TextIn's latest iteration of parsing technology, supporting various Bot developments. Hit the link below and start your exploration👇
https://www.coze.com/store/plugin/7397994540478578693