Academic Papers & Reports to Precision Markdown
Smarter than traditional OCR, 5-10x cheaper than LLM vision. Perfectly reconstruct complex multi-column layouts, multi-line LaTeX math formulas, and merged financial tables.
Authentic PDF vs Structured Markdown
Original publication layout on the left, structured Markdown restored by MarkifyDoc on the right.
Deep Residual Learning for Image Recognition
Abstract
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
1. Introduction
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40]. Driven by the significance of depth, a question arises: Is learning better networks as easy as stacking more layers? An obstacle to answering this question was the notorious problem of vanishing/exploding gradients.
Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer "plain" networks.
When deeper networks are able to start converging, a degradation problem has been exposed: with network depth increasing, accuracy gets saturated and then degrades rapidly.
Deep Residual Learning for Image Recognition
Abstract
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
1. Introduction
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40].
Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer "plain" networks.
Why Top Researchers & Analysts Choose MarkifyDoc
Accurately parses two-column papers, complex tables, and math formulas into clean Markdown
Academic LaTeX Preservation
Extracts both inline and multi-line equations into standard KaTeX/LaTeX ($ and $$ delimiters) without rendering artifacts.
Complex Merged Table Extraction
Precise geometric topological recognition for borderless, multi-row, and multi-column financial tables exported into valid GFM Markdown.
Multi-Column Reading Order
Eliminates cross-column stream mixing and paragraph dislocation. High-res figures and charts are auto-cropped into a downloadable ZIP.
Long Document Slicing & Rapid Parsing
Zero cold-start latency with instant second-level response. Automatically slices multi-hundred-page books and papers for parallel batch processing.
24-Hour Auto Physical Wipe
Strict data privacy: original files and outputs are permanently deleted after 24 hours. Your data is never used for AI training.
Standard OpenAPI & Webhooks
Secure SHA-256 authenticated API Keys with asynchronous webhook callbacks. Seamlessly integrates into RAG knowledge base ETL pipelines.
Flexible Pay-as-You-Go & Subscription Plans
1 Credit = 1 High-Precision Page · Auto Refund on Failed Pages
Free Plan
Great for casual reading and light research. 50 credits included.
Pro Plan
For active researchers, PhD candidates, and professionals.
Frequently Asked Questions
Everything you need to know about formula parsing, format compatibility, and data privacy