This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, TexT for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. TexT is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of TexT is demonstrated on an archival manuscript collection from well-known publicly available dataset.

Transcription of large-scale historical handwritten document images is a tedious task. Machine learning techniques, such as deep learning, are popularly used for quick transcription, but often require a substantial amount of pre-transcribed word examples for training. Instead of line-by-line word transcription, this paper proposes a simple training-free gamification strategy where all occurrences of each arbitrarily selected word is transcribed once, using an intelligent user interface implemented in this work. The proposed approach offers a fast and user-friendly semi-automatic transcription that allows multiple users to work on the same document collection simultaneously.

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.

Word spotting is popularly used for digitisation and transcription of historical handwritten documents. Recently, deep learning based methods have dominated the current state-of-the-art in learning-based word spotting. However, deep learning architectures such as Convolutional Neural Networks (CNNs) require a large amount of training data, and suffer from translation invariance. Capsule Networks (CapsNet) have been recently introduced as a data-efficient alternative to CNNs. This work explores the applicability of CapsNets for segmentation-based word spotting, and is the first such effort in the Handwritten Text Recognition (HTR) community to the best of authors' knowledge. The effectiveness of CapsNets will be empirically evaluated on well-known historical handwritten datasets using standard evaluation measures. The impact of varying amounts of training data on the recognition performance will be investigated, along with a comparison with the state-of-the-art methods.