A Review of Deep Learning Techniques in Document Image Word Spotting

From the early days of pattern recognition, word spotting have been important test beds for studying how well machines can perform better decision making. In recent years, word spotting have made dramatic advances with state-of-the-art techniques reaching high level of performance in real life applications. This word spotting domain have driven research by providing suitable yet well-defined challenges for pattern recognition and document analysis practitioners. We continue in this direction by covering extensive literature and new challenges in this domain with comparison of previous work. In particular, we have covered recent deep learning technique role in word spotting and future scope of word spotting with deep learning. We believe writing suitable review of word spotting will not only be crucial for understanding of this field in today era, but also in broader collaborative efforts, especially those with artificial intelligence based tasks. To facilitate future research in word spotting, we have discussed word spotting from learning environment, including its framework design with components as query phase, preprocessing stages, segmentation, feature extraction, feature representation and matching process strategies. Further, deep learning working and use in word spotting architecture has been discussed. The study also include an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.