Handwritten documents near-duplicate search for data intensive applications

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The problem of cheating in handwritten academic essays has become more significant over last several years. One of the cheating cases is submitting the same paper, photographed in different environment (for example, from another angle, in different light or in lower quality), or changed by means of automatic augmentation. The existing methods are not designed to work on large collections of handwritten documents. The proposed approach consists of three stages. The first stage is embedding generation, the second one is finding closest candidates in the collection of handwritten documents and the final one is similarity estimation between query image and each of candidates obtained at previous step. Our solution showed Recall@1 80% and 59% with FPR 4.8% and 5.5% on Synthetic and Real data respectively. The search latency is 5.5 seconds per query for the collection of 10 000 images. The results showed that the developed method is robust enough to work on large collections of handwritten documents.

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作者简介

K. Varlamova

Antiplagiat Company; Moscow Institute of Physics and Technology

编辑信件的主要联系方式.
Email: kvarlamova@ap-team.ru
俄罗斯联邦, Moscow; Moscow

М. Kaprielova

Antiplagiat Company; Moscow Institute of Physics and Technology; FRC CSC RAS

Email: kaprielova@ap-team.ru
俄罗斯联邦, Moscow; Moscow; Moscow

I. Potyashin

Antiplagiat Company; Moscow Institute of Physics and Technology

Email: potyashin@ap-team.ru
俄罗斯联邦, Moscow; Moscow

Yu. Chekhovich

Antiplagiat Company

Email: chehovich@ap-team.ru
俄罗斯联邦, Moscow

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1. JATS XML
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2. Formula 2.1

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3. Fig. 1. Example of images from the dataset with synthetic near-duplicates: original and generated near-duplicate

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4. Fig. 2. Image types from the HWR200 dataset from left to right: scan, light photo, and dark photo.

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5. Fig. 3. The process of extracting a signal from an image: conversion to grayscale, application of an adaptive threshold, signal extraction

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