Abstract
This paper presents our methodology and findings from three tasks across Optical Character Recognition (OCR) and Document Layout Analysis using advanced deep learning techniques. First, for the historical Hebrew fragments of the Dead Sea Scrolls, we enhanced our dataset through extensive data augmentation and employed the Kraken and TrOCR models to improve character recognition. In our analysis of 16th to 18th-century meeting resolutions task, we utilized a Convolutional Recurrent Neural Network (CRNN) that integrated DeepLabV3+ for semantic segmentation with a Bidirectional LSTM, incorporating confidence-based pseudolabeling to refine our model. Finally, for modern English handwriting recognition task, we applied a CRNN with a ResNet34 encoder, trained using the Connectionist Temporal Classification (CTC) loss function to effectively capture sequential dependencies. This report offers valuable insights and suggests potential directions for future research.
Abstract (translated)
本文介绍了我们在光学字符识别（OCR）和文档布局分析三个任务中采用的先进深度学习技术的方法论及发现。首先，对于来自死海古卷的历史希伯来文碎片，我们通过大量数据增强扩展了我们的数据集，并使用Kraken和TrOCR模型提高了字符识别精度。在16至18世纪会议决议文本分析的任务中，我们运用了一种结合了DeepLabV3+语义分割与双向长短期记忆网络（BiLSTM）的卷积递归神经网络（CRNN），并通过基于置信度的伪标签来优化模型。最后，在现代英文手写识别任务中，我们采用了一个带有ResNet34编码器的CRNN，并通过连接主义时间分类（CTC）损失函数进行训练，以有效捕捉序列依赖关系。 本文提供了有价值的见解，并为未来的研究方向提出了建议。
URL
https://arxiv.org/abs/2508.10356
