# Seeing Colors: Learning Semantic Text Encoding for Classification

Source: [Seeing Colors: Learning Semantic Text Encoding for Classification](<https://www.sota2.com/research/paper/68fa64c61e209131cea0680f>)

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

Data updated: 2025-10-25T21:11:58.696Z

Authors: Shah Nawaz, Alessandro Calefati, Muhammad Kamran Janjua, Ignazio Gallo

Published: 2018-08-31T15:55:56.000Z

## Abstract

The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we present a novel text classification method which converts a text document into an encoded image, using word embedding and capabilities of Convolutional Neural Networks (CNNs), successfully employed in image classification. We evaluate our approach by obtaining promising results on some well-known benchmark datasets for text classification. This work allows the application of many of the advanced CNN architectures developed for Computer Vision to Natural Language Processing. We test the proposed approach on a multi-modal dataset, proving that it is possible to use a single deep model to represent text and image in the same feature space.

## 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/1808.10822>)

[PDF](<https://arxiv.org/pdf/1808.10822.pdf>)