Document layout analysis

About: Document layout analysis is a research topic. Over the lifetime, 1462 publications have been published within this topic receiving 34021 citations.

Practical Segmentation Methods for Logical and Geometric Layout Analysis to Improve Scanned PDF Accessibility to Vision Impaired

Abstract: The use of electronic documents has rapidly increased in recent decades and the PDF is one the most commonly used electronic document formats. A scanned PDF is an image and does not actually contain any text. For the vision–impaired user who is dependent upon a screen reader to access this information, this format is not useful. Thus addressing PDF accessibility through assistive technology has now become an important concern. PDF layout analysis provides precious formatting information that supports PDF component classification. This classification facilitates the tag generation. Accurate tagging produces a searchable and navigable scanned PDF document. This paper describes several practical segmentation methods which are easy to implement and efficient for PDF layout analysis so that the scanned PDF document can be navigated or searched using assistive technologies.

Systems and methods for enabling manual classification of unrecognized documents to complete workflow for electronic jobs and to assist machine learning of a recognition system using automatically extracted features of unrecognized documents

Abstract: A method in a document analysis system automatically extracts image and text features from each received electronic document and compares the extracted features with feature sets associated with each category of document to determine whether the document is recognizable as belonging to a document category. If an electronic document is recognized as belonging to one of the document categories, the method classifies the electronic document as belonging to that document category. If, however, an electronic document is unrecognized, the method submits the unrecognized document to a learning phase, in which the unrecognized document is presented to a human trainer for manual classification of the unrecognized electronic document into a document category, and automatically modifies at least one of the features and the weights of the feature set of the document category corresponding to the manually-classified electronic document using the automatically extracted features of the manually-classified document.

Deep Arabic document layout analysis

Abstract: Document layout analysis (DLA) is an essential step for Optical Character Recognition Systems (OCR). The text of the document fed to the OCR must be extracted first and isolated from images if exist. The DLA task is difficult as there is no fixed layout for all documents, but instead, there are several layouts. There are various approaches for DLA for various different languages. In this paper, some of the previous techniques used in this field will be listed and then we will discuss the proposed method that depends on deep learning for documents' text localization. We used Arabic Printed Text Image database (APTI [19]), ImageNet [18] and a dataset collected from different Arabic newspapers for training and evaluation.

On the Evaluation of Handwritten Text Line Detection Algorithms

Abstract: Even if numerous text line detection algorithms have been proposed, the algorithms are usually compared on a single database and according to a single metric. In this paper, we study the performance of four different text line detection algorithms, on four databases containing very different documents, and according to three metrics (Zone Map, ICDAR and recognition error rate). Our goal is to provide a more comprehensive empirical evaluation of handwritten text line detection methods and to identify what are the key points in the evaluation. We show that the different algorithms yield very different results depending on the type of documents and that two of them are constantly better than the others. We also show that the Zone Map and the ICDAR metric are strongly correlated, but the Zone Map metric provides greater detail on the error types. Finally we show that the geometric metrics are correlated to the recognition error rate on easy to segment databases, but this has to be confirmed on difficult documents.

Numbered sequence detection in documents

Abstract: We present in this work a method to detect numbered sequences in a document. The method relies on the following steps: first, all potential "numbered patterns" are automatically extracted from the document. Secondly, possible coherent sequences are built using pattern incrementality (called incremental relation). Finally possible wrong links between items are corrected using the notion of optimization context. An evaluation of the method is presented and weaknesses and possible improvements are discussed.