# Multidomain Document Layout Understanding using Few Shot Object Detection

Source: [Multidomain Document Layout Understanding using Few Shot Object Detection](<https://www.sota2.com/research/paper/68fa64b91e209131cea060ff>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:36.607Z

Authors: Pranaydeep Singh, Srikrishna Varadarajan, Ankit Narayan Singh, Muktabh Mayank Srivastava

Published: 2018-08-22T12:23:51.000Z

## Abstract

We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1808.07330>)

[PDF](<https://arxiv.org/pdf/1808.07330.pdf>)