用深度学习破解古老埃塞俄比亚文字手写识别难题
Learning based Ge'ez character handwritten recognition
- 分两阶段：先用CNN识单字，再用LSTM识整词
- 在HHD-Ethiopic数据集上超越8种顶尖方法和人工水平
- 为古籍数字化与文化遗产保护提供关键技术
Ge'ez是具有文化与历史重要性的古老埃塞俄比亚文字，但长期以来在手写识别研究中被忽视，阻碍了珍贵手稿的数字化进程。本研究通过使用卷积神经网络（CNN）和长短期记忆网络（LSTM），构建了先进的Ge'ez手写识别系统。采用两阶段识别流程：首先训练CNN识别单个字符，作为特征提取器；随后将提取特征输入LSTM系统进行单词级识别。该双阶段方法在HHD-Ethiopic数据集上的表现达到新纪录，优于八种现有先进方法（包括SVTR、ASTER等），甚至超过人工识别性能。本研究显著推进了Ge'ez文化遗产的保存与可及性，对历史文献数字化、教育工具开发及文化传承具有重要意义。代码将在论文录用后公开。
原文摘要 · Abstract (English)
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research significantly advances the preservation and accessibility of Ge'ez cultural heritage, with implications for historical document digitization, educational tools, and cultural preservation.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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。