In daily office work, we noticed that users are often troubled by issues such as "garbled text extraction", "distorted formatting after conversion", and "uneditable scanned PDFs" when handling PDFs—and this is the original intention behind developing DeepPDF. As a core member of the development team, I want to share the story of this intelligent PDF tool with you today, focusing on its functional logic and technical implementation.
Core Features of DeepPDF: Resolving PDF Processing Pain Points with "Intelligence"
- Multi-Dimensional Content Parsing
Unlike traditional tools that only extract text, we have integrated the LayoutLMv3 Document Understanding Model, which can accurately identify tables, images, formulas, and even handwritten annotations in PDFs. For instance, when processing financial report files, it can directly convert nested tables into structured data. It also supports semantic recognition for 12 languages, solving the problem of multi-language PDF parsing.
- High-Fidelity Format Conversion
"Consistent formatting after conversion" is a core user demand. We have made special optimizations for formats such as Word, Excel, and PPT through a pixel-level comparison algorithm, ensuring that font styles and chart positions are completely consistent with the original file. During the testing phase, we increased the typesetting consistency between the converted file and the original file from the industry average of 80% to 98%, completely solving the "garbled conversion" problem.
- Lightweight Collaboration Features
Considering team scenarios, we have added online annotation and version control modules, supporting real-time multi-person PDF annotation without the need to download a client. At the same time, we use AES-256 encrypted storage to strike a balance between convenience and data security.
Development Journey: Key Milestones from Technology Selection to Implementation Optimization
- Technology Stack Selection
For the frontend, we chose Vue3+Vite, valuing its lightweight features—PDF users often handle large files, and page loading speed directly affects the experience. For the backend, we use Python+FastAPI, combined with PyTorch for AI model deployment, balancing development efficiency and inference performance. For the storage layer, we selected an object storage service to solve the scalability issue of large file storage.
- Overcoming Core Challenges
We encountered two major bottlenecks during development: first, upload timeouts for large files over 50MB. We solved this by using a chunked upload + resumable upload mechanism, splitting files into 1MB chunks and increasing the upload success rate from 70% to 99%. Second, slow parsing speed—with the help of GPU-accelerated inference and hot file caching, we reduced the average parsing time from 15 seconds to under 3 seconds.
- User Feedback-Driven Iteration
During the beta testing phase, 500 users reported that "uneditable scanned PDFs" was a high-frequency demand. We urgently integrated the Tesseract OCR engine, optimizing the text recognition accuracy for scanned PDFs from 85% to 98%. After this feature was launched, the user retention rate increased by 20% directly.
The process of developing DeepPDF has been a journey of using technology to solve real-world needs. In the future, we will optimize mobile adaptation and AI summarization features. If you have usage suggestions or need technical discussions, feel free to share in the comments section!
Top comments (0)