Handwritten word recognition from document images using deep learning is an active research area in the field of Document Image Analysis and Recognition. In the present era of Big data, since more and more documents are being generated and archived in the compressed form to provide better storage and transmission efficiencies, the problem of word recognition in the respective compressed domain without decompression becomes very challenging. The traditional methods employ decompression and then apply learning algorithms over them, therefore, novel algorithms are to be designed in order to apply learning techniques directly in the compressed representations/domains. In this direction, this research paper proposes a novel HWRCNet model for handwritten word recognition directly in the compressed domain specifically focusing on JPEG format. The proposed model combines the Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) based Recurrent Neural Network (RNN). Basically, we train the model using JPEG compressed word images and observe a very appealing performance with $89.05\%$ word recognition accuracy and $13.37\%$ character error rate.

翻译：利用深度学习从文档图像中识别手写单词是文档图像分析与识别领域的一个活跃研究方向。在大数据时代，越来越多的文档以压缩形式生成并归档，以实现更优的存储与传输效率，因此，在不经解压缩的情况下直接对相应压缩域进行单词识别的问题变得极具挑战性。传统方法先解压缩，再对解压后的数据应用学习算法，因此需设计新型算法，以便直接在压缩表示/压缩域中应用学习技术。基于此，本文提出一种新型HWRCNet模型，专门针对JPEG格式，直接在压缩域中实现手写单词识别。该模型结合了卷积神经网络（CNN）和基于双向长短期记忆（BiLSTM）的循环神经网络（RNN）。我们使用JPEG压缩单词图像对模型进行训练，获得了令人满意的性能：单词识别准确率为$89.05\%$，字符错误率为$13.37\%$。