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.
Manual transcription of historical handwritten documents requires highly skilled experts, and is typically a time consuming process. Manual transcription is clearly not a feasible solution due to large amounts of data waiting to be transcribed. Fully automatic transcription using HTR techniques offers a cost-effective alternative, but often fails in delivering the required level of transcription accuracy @cite_3 . Instead, semi-automatic or semi-supervised transcription methods have gained importance in the recent past @cite_3 @cite_14 @cite_28 @cite_5 @cite_13 .
@cite_14 @cite_28 @cite_3 @cite_5 @cite_13
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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. 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. 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. Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.