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Bewertet mit 4. Download Desktop Version. Hintergrund entfernen. Seiten drehen. Seiten gerade richten. To conclude, click on “Submit. Convertio is another web application where you can perform the OCR process to PDF documents but, if your document has more than ten pages, you will have to register first.

Now I will show you how to use it. When your PDF finishes uploading, you must specify the document language and the desired output format. When finished, simply click on “Recognize. Some applications allow you to carry out OCR processes without needing an internet connection, but there are some things that you should keep in mind.

Although Preview is a very practical tool for viewing, adding signatures, and adding annotations to PDF documents, this tool is not capable of OCR processes. This depends on the version of Adobe Acrobat Reader you have. However, you can export your document in text format. This will not be as effective as the OCR tool, but it will be useful if you only need to transcribe text.

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PDF Expert is delightfully easy to use. Since the OCRed tokens are looked up in the lexicon by using some kind of a fuzzy match, there is a chance that run-on words are corrected. Kolak et al. By al- lowing for exactly one split per word in the lexicon which is derived from a supervised corpus , disrupted words can be found if their parts match the split lexicon entries.

Depending on the goal of the task, the resulting units might be documents, para- graphs, sentences, words, or morphs. It is usually referred to as word segmentation. For example, Gold- water et al. Although tokenisation seems to be quite straightforward a task, there is a number of is- sues to be addressed, such as ambiguous punctuation marks e. Kolachina et al. While general approaches are discussed in handbooks e. Wrenn et al. Segmentation boundaries are thus introduced de- pending on the distribution of character transitions, counted in a text corpus.

Considering the conceptual simplicity of the approach and the fact that it is fairly unsupervised, these results are very promising. Computational morphology is concerned with the modelling of linguistic morphology, i.

One of the most basic ways to approach a words internal structure is to split it into meaningful components, its morphs or, if their function and variance are considered as well, its morphemes.

Example 2. Analogously, LSV can be counted backwards, i. Hafer and Weiss , p. Peak and plateau A boundary is inserted wherever an LSV count is greater than or equal to both its next neighbours.

If the increase is followed by a decrease, then the LSV curve forms a peak at this position. Entropy, in information theory, is a measure for the skewedness of a probability distribution. It is maximal if all possible outcomes have the same probability, and continually decreases as the distribution gets more skewed. In NLP, typically, log2 or log10 is chosen.

If there is one successor character that consumes a majority of the probability, e. Goldsmith , and successors has created a whole framework of MDL-based segmentation systems. Al- though the work is done by hand, it can be regarded as an unsupervised approach nonetheless, as it is based purely on counting characters in a dataset. However, the work focused linguistic insight, rather than a modern-style NLP tool. While, technically speaking, no anno- tations are needed, the costs for creating training data of the required quality presumably cancel out the economical advantages of an unsupervised approach.

For this purpose, he adapts the LSV method to operate on sequences of morphemes, rather than phonemes or graphemes. In fact, he also performs experiments using entropy, anticipating the LSE approach reported by Hafer and Weiss Furthermore, at the end of the paper, the author reports exemplarily on an adaption of the approach to work with grapheme sequences, i.

Although the results look somewhat odd, they do foreshadow the potential of unsupervised text segmentation. Facing the problem that morpheme sequences are more diversely distributed than phoneme sequences, and that it is not clear which higher-level syntactic unit should be taken to form a test sample, the author chooses to divide the cor- pus into overlapping subsequences of length k. Gammon performs a quantitative evaluation and also criticises Harris for not doing so. As for the usefulness, the evaluation is an issue.

It is even harder to tell what the system should have produced. Instead of precision, thus, a measure of pseudoprecision is introduced, which is based on the number of blunders the system made. In order to compute LSV counts in continuous text, the text is divided into subsequences, just as did Gammon Figure 2.

In order to calculate both LSV and LPV, two tries are constructed: one for the forward, and one for the backward reading of the text. Section 3. A profound description of the functionality of the implemented system is given in sections 3. Both comprise of printed, German documents with a uniform source. Out of this information, I used only the sectioning of the text body into articles and paragraphs. In general NLP terminology, the articles correspond to documents.

In this thesis, however, the subdivision into documents is only used for evaluating the test set. Otherwise, the unit of context relevant for a task either expands to a paragraph or to the complete collection. Creating a test set for evaluating the correction system involved only a small amount of manual work.

In the same manner as the test set, I built a development-test set from another 60 articles, which I used for parameter estimation during development. Typically, a decision consists of a handful of paragraphs.

For this thesis, I created a corpus from the raw OCR output, discarding all post- processing steps that have been performed in the digitisation project, such as de- cision boundary detection, title extraction, and post-correction. Where paragraph boundaries had been detected by the OCR system, I kept them, which gave a basic text sectioning below page level.

In fact, the evaluation of a random sample yielded a total character accuracy rate of According to section 2. In order to obtain a test set, a sample of clean text was needed, alongside its OCRed counterpart. Fortunately, I was able to re-use a selection of decisions which had been manually corrected during the RRB-Fraktur digitisation project.

Since the test sets consist of decision documents, while the raw text collection is divided into pages, I removed complete pages from the collection when building the training set, rather than manually asserting decision boundaries. Table 3. Character accuracy is the portion of correctly recognised characters. Word accuracy is generally lower than character accuracy, since a single misrecognised character renders the complete word as an error.

But inspection of the data also showed that there are regions with higher-than-average error density which highly increase document accuracy in some of the RRB decisions. Figure 3. Two major steps can be distinguished: In a preprocessing step, all text is segmented, using an unsu- pervised method. It is presented in section 3. In order to achieve high agreement with traditional tokenisation, Wrenn et al. However, depending on how the segmented text is processed by a succeeding NLP application, there might be no need to limit unsupervised segmentation to producing either words or morphs.

In an extrinsic evaluation like this, the usefulness of the intermediate step—the segmented text—is only determined by the improve- ments of the complete system.

Sec- tion 3. It is important to note that the terms train- ing and test data are determined by their use in the subsequent correction step. Since all data is involved both at training and prediction time, there is no useful distinction between training and test data from the segmentation perspective. Another important thing to note is the recognition quality of the segmented ma- terial.

But while the training data are a ground-truth text in the case of the TA collection supervised correction , it is just as noisy as the test set in the RRB case unsupervised correction. All overlapping subsequences of length k of all paragraphs in the text collection are counted. Every time the window is moved, the character sequence visible is recorded.

As the segmentation approach requires forward and backward counts of the se- quences, two tries are always needed. When the character tries are populated at the end of the learning phase, they are ready to serve as a knowledge basis during segmentation. A lookup of any sequence x is guaranteed to reach nx by a path of edges with positive weights, if x has been seen in the learning phase.

Otherwise, e. In the well-known example 1. Hence, case sensitivity was made a target of the experiments by including both a case sensitive and a lower-cased version of the learning method in the experiment set-up, such as to measure the impact of this binary parameter with regard to the correction task. In the case sensitive case, the characters are added to the trie just as they are found in the text.

When the count of a character sequence is needed during the segmentation phase, a lower-cased copy of the sequence is looked up in the trie. In order to limit the combinatorial complexity of the experiments, I did not further investigate these ideas.

It determines a set of boundaries, based on the counts from the learning phase, which are stored in the forward and backward trie. Transitions are numbered starting from 1, with the 1st transition reaching from the 1st to the 2nd character.

If the fragility reaches or exceeds the threshold, a boundary is inserted. If there is a peak at transition i, then the LSV drop to both its neighbours is summed. Empirically, with a threshold approaching the alphabet size, segment boundaries already became quite seldom. In the terminology of Hafer and Weiss , p. However, I can see no reason for skipping the learning phase for any text targeted at segmentation.

In the processing pipeline of the implemented correction system, the initial doc- uments are split into paragraphs and further into segments or tokens in the beginning, and in the end they are rejoined into documents for evaluation. However, tra- ditional tokens do not contain whitespace. Segment Lengths and Counts All of these parameters have an impact on the resulting segmentation.

Examples 3. Tables 3. For the entropy scheme, 1, 3, 5, and 7 were investigated. More precisely, adding context increases the number of segments at low thresholds, but reduces it at high thresholds.

As context is added, the LSV values go down in general, but the reduction is not evenly distributed across all positions, which might lead to the formation of new peaks. A possibility to avoid this loss of segmentations would be to inversely scale the threshold parameter to the increased window size. It would, however, require more focused investigation. In LSV-peak, lower-casing categorically results in a reduced number of segments.

As I strive for an approach that uses a minimal amount of knowledge, I chose a correction algorithm that requires only a small number of free parameters, and which is theoretically well-founded. For this reason, the chosen correction system closely follows the hidden Markov model approach proposed by Tong and Evans In the detection procedure, a statistical n-gram model is built from the training data.


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