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.
In practical scenarios, such methods are not appropriate as a system should ideally accept a full document page as an input and generate full transcription of the words as an output. An end-to-end system for handwritten text transcription is presented in @cite_3 @cite_14 that also uses HMM-based text image modeling with interactive computer assisted transcription. The transcription method proposed in this work addresses these issues and introduces @math for quick transcription of handwritten text using a segmentation-free word spotting algorithm @cite_20 . The following section explains the proposed method and its advantages in detail.
@cite_14 @cite_20 @cite_3
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An effective approach to transcribe handwritten text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. This approach has been recently implemented in a system prototype called GIDOC, in which standard speech technology is adapted to handwritten text (line) images: HMM-based text image modelling, n-gram language modelling, and also confidence measures on recognized words. Confidence measures are used to assist the user in locating possible transcription errors, and thus validate system output after only supervising those (few) words for which the system is not highly confident. Here, we study the effect of using these partially supervised transcriptions on the adaptation of image and language models to the task. To provide access to the contents of the document collections that are being digitized, transcription is required. Unfortunately manual transcription is generally too expensive and, in most cases, current automatic techniques fail to provide the required level of accuracy. An alternative that can speed up and lower the cost of this process is the use of computer assisted, interactive techniques. These techniques work at line-level thus the transcription task assumes that the page images have been correctly decomposed into the relevant text line images. In this paper we present an end-to-end system that takes as input a page image and provides a fully correct transcript with the help of user interaction. The system automatically performs the text block and text line detection to be fed into the interactive computer assisted transcription. Experiments carried out show that the expected amount of user effort needed to produce perfect transcripts, can be reduced by using the proposed end-to-end system. The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process. In this paper, an off-line, text independent system for writer identification and verification of handwritten text lines using Hidden Markov Model (HMM) based recognizers is presented. For each writer, an individual recognizer is built and trained on text lines of that writer. This results in a number of recognizers, each of which is an expert on the handwriting of exactly one writer. In the identification and verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the generated output. These scores are sorted and the resulting ranking is used for both identification and verification. Several confidence measures are defined on this ranking. The proposed writer identification and verification system is evaluated using different experimental setups.