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research significantly advances the preservation and accessibility of Ge'ez cultural heritage, with implications for historical document digitization, educational tools, and cultural preservation.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.
Abstract (translated)
古埃塞俄比亚文字盖兹文（Ge'ez），作为一种具有文化和历史意义的古老书写系统，长期以来在手写识别研究中被忽视，这阻碍了珍贵手稿的数字化进程。我们的研究通过开发一个基于卷积神经网络（CNNs）和长短时记忆（LSTM）网络的最先进的盖兹文手写识别系统来解决这一问题。我们的方法采用两阶段识别过程：首先训练一个 CNN 来识别单个字符，然后将该 CNN 作为特征提取器用于基于 LSTM 的单词识别系统。这种双阶段识别方法在盖兹文手写识别中达到了新的最高分，超越了包括 SVTR、ASTER 在内的八种最先进的方法以及人类的表现，这一结果是在 HHD-Ethiopic 数据集上进行测量得出的。这项研究显著推进了盖兹文文化遗产的保护和访问性，在历史文档数字化、教育工具及文化保存方面具有深远影响。代码将在接受后发布。
URL
https://arxiv.org/abs/2411.13350
