Writing Style Invariant Deep Learning Model for Historical Manuscripts Alignment

TL;DR

This paper introduces a writer-independent deep learning model for historical manuscript alignment that generalizes well across unseen writing styles, achieving over 92% accuracy in cross-validation tests.

Contribution

The paper presents a novel deep learning model trained on multiple writing styles, capable of accurately aligning manuscripts with unseen styles, along with a new dynamic sliding window alignment algorithm.

Findings

Average accuracy of 92.17% in cross-validation

Model generalizes to unseen writing styles

Effective handling of complex alignment cases

Abstract

Historical manuscript alignment is a widely known problem in document analysis. Finding the differences between manuscript editions is mostly done manually. In this paper, we present a writer independent deep learning model which is trained on several writing styles, and able to achieve high detection accuracy when tested on writing styles not present in training data. We test our model using cross validation, each time we train the model on five manuscripts, and test it on the other two manuscripts, never seen in the training data. We've applied cross validation on seven manuscripts, netting 21 different tests, achieving average accuracy of $\%92.17$. We also present a new alignment algorithm based on dynamic sized sliding window, which is able to successfully handle complex cases.