You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: 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.

[3]: 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.
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 [3] [1] 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 [2] . The following section explains the proposed method and its advantages in detail.