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：利用深层学习,从文档图像中手写字识别,是文件图像分析和识别领域的一个积极研究领域。在目前大数据时代,由于越来越多的文件以压缩形式生成和存档,以提供更好的存储和传输效率,因此,在相关压缩域中,不压缩的单词识别问题变得非常棘手。传统方法采用降压,然后在它们上应用学习算法,因此,将设计新的算法,以便直接在压缩显示/域中应用学习技术。在这方面,本研究论文提出一个新的HWRCNet模型,用于在压缩域中直接手写字识别,具体侧重于JPEG格式。拟议的模型将革命神经网络(CNN)和基于常规神经网络(BILSTM)的双调短期内存(BILSTM)结合起来。基本上,我们用JEG压缩单词图像来培训模型,并用89.05美元单词识别精确度和13.37美元字符错误率来观察非常有吸引力的性。