Learning based Ge'ez character handwritten recognition

TL;DR

This paper presents a novel deep learning system combining CNNs and LSTMs for recognizing handwritten Ge'ez script, significantly improving accuracy over previous methods and aiding cultural heritage digitization.

Contribution

The study introduces a dual-stage recognition system for Ge'ez handwriting using CNNs and LSTMs, achieving top performance and surpassing existing methods and human accuracy.

Findings

Achieved new top scores in Ge'ez handwriting recognition

Outperformed eight state-of-the-art methods and human performance

Contributed to preservation of Ge'ez cultural heritage

Abstract

Ge'ez, an ancient Ethiopic script of cultural and historical significance, has been largely neglected in handwriting recognition research, hindering the digitization of valuable manuscripts. Our study addresses this gap by developing a state-of-the-art Ge'ez handwriting recognition system using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Our approach uses a two-stage recognition process. First, a CNN is trained to recognize individual characters, which then acts as a feature extractor for an LSTM-based system for word recognition. Our dual-stage recognition approach achieves new top scores in Ge'ez handwriting recognition, outperforming eight state-of-the-art methods, which are SVTR, ASTER, and others as well as human performance, as measured in the HHD-Ethiopic dataset work. This research significantly advances the preservation and accessibility of Ge'ez cultural heritage, with implications for historical document digitization, educational tools, and cultural preservation. The code will be released upon acceptance.