# Measuring Human Perception to Improve Handwritten Document Transcription

## TL;DR

This paper introduces a novel loss function incorporating psychophysical measurements of human perception to enhance deep learning models for handwritten document transcription, demonstrating improved accuracy on multiple datasets including historical manuscripts.

## Contribution

It proposes a new loss formulation that integrates human perceptual data into training deep neural networks for handwriting recognition, applicable to modern and historical documents.

## Key findings

- Performance improved on IAM and RIMES datasets
- Demonstrated feasibility on 9th-century Latin manuscripts
- Applicable across different neural network architectures

## Abstract

The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition. For instance, measured reaction time can indicate whether a visual stimulus is easy for a subject to recognize, or whether it is hard. In this paper, we consider how to incorporate psychophysical measurements of visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can enforce consistency with human behavior. As a case study to assess the viability of this approach, we look at the problem of handwritten document transcription. While good progress has been made towards automatically transcribing modern handwriting, significant challenges remain in transcribing historical documents. Here we describe a general enhancement strategy, underpinned by the new loss formulation, which can be applied to the training regime of any deep learning-based document transcription system. Through experimentation, reliable performance improvement is demonstrated for the standard IAM and RIMES datasets for three different network architectures. Further, we go on to show feasibility for our approach on a new dataset of digitized Latin manuscripts, originally produced by scribes in the Cloister of St. Gall in the the 9th century.