Towards Semi-supervised Transcription of Handwritten Historical Weather Reports

Richarz, Jan; Vajda, Szil ́rd; Fink, Gernot A.

doi:10.1109/das.2012.91

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Each document had five columns, and each column had a number of rows. Richarz et al [2] also implemented a semi-supervised OCR system on historical weather reports with printed tables. They scanned 58 pages and applied segmentation by using the printed tables.…”

Section: Related Workmentioning

confidence: 99%

“…For digitalizing the historical documents, page segmentation of different areas is a critical process for further document analysis [ 1 ]. Example applications of historical document processing could be historical weather analysis [ 2 ], personnel record analysis [ 3 ], and digitization of music score images (OMR) [ 4 ]. Page segmentation techniques analyze the document by dividing the image into different regions such as backgrounds, texts, graphics, and decorations [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

Can

Kabadayı

2020

J. Imaging

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Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

Can

Kabadayı

2020

J. Imaging

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“…In addition to handwritten documents stored in historic archives there are processes, like manually filling out forms, which still depend on pen and paper. However, accessing the information stored in those documents requires time and manpower [9]. By digitizing those documents, the advantages of digitally stored information, such as ease of access, can be exploited for handwritten documents [9].…”

Section: Introductionmentioning

confidence: 99%

Digit Recognition in Handwritten Weather Records

Keglevic,

Sablatnig

2013

Preprint

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This paper addresses the automatic recognition of handwritten temperature values in weather records. The localization of table cells is based on line detection using projection profiles. Further, a stroke-preserving line removal method which is based on gradient images is proposed. The presented digit recognition utilizes features which are extracted using a set of filters and a Support Vector Machine classifier. It was evaluated on the MNIST and the USPS dataset and our own database with about 17,000 RGB digit images. An accuracy of 99.36% per digit is achieved for the entire system using a set of 84 weather records.

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“…Lots of work deals with machine-printed table recognition [10,3,7]. Gatos et al [7], make use of the complete table rulings to recognize the table structure method of tabular structure recognition for their semisupervised transcription system in handwritten historical weather reports [15]. Making use of the pre-printed table substrate, they use the Hough transform to detect the horizontal and vertical rulings that constitute the tabular structures.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Tabular Structure Detection and Recognition in Noisy Handwritten Documents

Chen

Lopresti

2012

2012 International Conference on Frontiers in Handwriting Recognition

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Tabular structure detection and recognition can be a valuable step in the analysis of unstructured documents. The noisy handwritten documents we try to analyze may contain pre-printed rulings as the substrate, hand-drawn rulings, machine-printed text, handwritten text, and signatures, in addition to the tabular structures which we wish to decompose into basic cells, rows, and columns. Although work has been done to machineprinted documents, noisy handwritten documents may require modified and/or new techniques. In this work, we try to detect and decompose tabular structures into 2-D grids of table cells simultaneously. First, we detect "key points" that help determine the physical and logical structure of tables. Then, we make use of the 2-D grid assumption to build grids of key points. Finally, we extract structural features for the Min-Cut/Max-Flow algorithm to recognize tabular structures. Experiments on 22 tables which contain 584 table cells show a cell precision of 100% and a cell recall of 93.3%.

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Towards Semi-supervised Transcription of Handwritten Historical Weather Reports

Cited by 10 publications

References 22 publications

CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

Digit Recognition in Handwritten Weather Records

Model-Based Tabular Structure Detection and Recognition in Noisy Handwritten Documents

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