Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets.

The automatic recognition of historical handwritten documents is still considered a challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.