Recently, TextIn's PDF to Markdown plugin officially launched on the Coze platform.

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

To test how the parsing plugin performs in your specific use case, you can directly chat with the bot to try out the PDF to Markdown conversion.

Additionally, the TextIn team has provided simple Workflow examples for reference. Users needing to build workflows can copy and use these directly.

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

•Large files: Currently supports files up to 500MB through the synchronous interface, with plans to increase this limit

•Long documents: Currently supports up to 1000 pages, with development plans aiming for 5000 pages

•High speed: Quickly parses hundred-page PDFs without long wait times Moreover, each user gets a free 1000-page quota, allowing "quota freedom" for small-scale parsing tasks.

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

Due to the visual encoding nature of PDF files, their content is difficult to extract or edit. For a long time, PDFs have often become the endpoint where knowledge "sleeps." In the era of LLMs( Large Language Models), building "smart" AI requires not only computing power but also high-quality corpora. The shortage of Chinese language corpora has become a focus of attention in the industry. Currently, a large amount of high-quality Chinese language data exists in books, papers, research reports, and corporate documents. Complex layout structures limit the processing of training corpora for LLMs(large language models) and the application capabilities of LLMs( Large Language Models) in Question-Answering over Documents (Docs-QA).

Document parsing technology enables machines to recognize various elements in documents, better process multiple types of data such as text, tables, and images, restore the reading order of documents, and serve the development of various AI applications and intelligent agents.

Through physical layout analysis and logical layout analysis techniques, TextIn's document parsing can accurately identify various elements in documents and understand their logical relationships. Physical layout analysis focuses on visual features and document layout, mainly tasked with aggregating highly related text into one area, such as a paragraph or a table, and is modeled using object detection tasks, using regression-based single-stage detection models for fitting, thus obtaining various layout methods in the document. Logical layout analysis focuses on semantic feature analysis, mainly tasked with modeling different text blocks based on semantics, for example, forming a directory tree structure through the hierarchical relationship of semantics.

TextIn has deep technical accumulation in the field of document intelligence, developing layout analysis capabilities based on text and table recognition OCR technology. With the development of deep learning technology, layout analysis capabilities have been significantly improved, making it possible to process complex document layouts. 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 location and bounding boxes of different elements in the document can be determined, providing a foundation for subsequent analysis and processing.

•Element classification: Classify the detected elements, 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 for subsequent structure parsing and semantic understanding.

•Structure parsing: Based on element detection and classification, perform document structure parsing to identify relationships and hierarchical structures between different elements in the document. This includes the correspondence between text paragraphs and titles, relationships between different fields in tables, etc. Deep learning models can achieve automatic parsing and understanding of document structure through analysis of document layout and semantic information.

•Layout correction: Perform layout correction on the detected document elements to make their position and arrangement in the overall document more reasonable and uniform. This step can include operations such as text alignment, image correction, table alignment, etc., to improve the readability and aesthetics of the document.

Currently, the "PDF to Markdown" Coze plugin is connected with TextIn's latest iteration of parsing technology, supporting various Bot developments. Copy the link and try it now 👉 https://www.coze.com/store/plugin/7397994540478578693