Lifelong learning for text retrieval and recognition in historical handwritten document collections
This addresses the problem of scalable and adaptive text recognition for historians and archivists dealing with variable, user-labeled historical documents, but it is incremental as it builds on existing deep learning and retrieval methods.
The paper tackles the challenge of building a lifelong-learning system for retrieving and recognizing text in large, diverse historical handwritten document collections, introducing the 'ball-park principle' to manage the transition from sparse to dense labeling stages.
This chapter provides an overview of the problems that need to be dealt with when constructing a lifelong-learning retrieval, recognition and indexing engine for large historical document collections in multiple scripts and languages, the Monk system. This application is highly variable over time, since the continuous labeling by end users changes the concept of what a 'ground truth' constitutes. Although current advances in deep learning provide a huge potential in this application domain, the scale of the problem, i.e., more than 520 hugely diverse books, documents and manuscripts precludes the current meticulous and painstaking human effort which is required in designing and developing successful deep-learning systems. The ball-park principle is introduced, which describes the evolution from the sparsely-labeled stage that can only be addressed by traditional methods or nearest-neighbor methods on embedded vectors of pre-trained neural networks, up to the other end of the spectrum where massive labeling allows reliable training of deep-learning methods. Contents: Introduction, Expectation management, Deep learning, The ball-park principle, Technical realization, Work flow, Quality and quantity of material, Industrialization and scalability, Human effort, Algorithms, Object of recognition, Processing pipeline, Performance,Compositionality, Conclusion.