Hierarchical decomposition of handwritten<br /> manuscripts layouts

http://www.springerlink.com/content/k6741wt1028l7310/International audienceIn this paper we propose a new approach to improve electronic editions of literary corpus, providing an efficient estimation of manuscripts pages structure. In any handwriting documents analysis process, structure recognition is an important issue. The presence of variable inter-line spaces, of inconstant base-line skews, overlappings and occlusions in unconstrained ancient 19th handwritten documents complicates the structure recognition task. Text line and fragment extraction is basedon the connexity labelling of the adjacency graph at different resolutionlevels, for borders, lines and fragments extraction

Machine Learning for handwriting text recognition in historical documents

Olmos ABSTRACT In this thesis, we focus on the handwriting text recognition task over historical documents that are difficult to read for any person that is not an expert in ancient languages and writing style. We aim to take advantage and improve the neural networks architectures and techniques that other authors are proposing for handwriting text recognition in modern handwritten documents. These models perform this task very precisely when a large amount of data is available. However, the low availability of labeled data is a widespread problem in historical documents. The type of writing is singular, and it is pretty expensive to hire an expert to transcribe a large number of pages. After investigating and analyzing the state-of-the-art, we propose the efficient application of methods such as transfer learning and data augmentation. We also contribute an algorithm for purging mislabeled samples that affect the learning of models. Finally, we develop a variational auto encoder method for generating synthetic samples of handwritten text images for data augmentation. Experiments are performed on various historical handwritten text databases to validate the performance of the proposed algorithms. The various included analyses focus on the evolution of the character and word error rate (CER and WER) as we increase the training dataset. One of the most important results is the participation in a contest for transcription of historical handwritten text. The organizers provided us with a dataset of documents to train the model, then just a few labeled pages of 5 new documents were handled to adjust the solution further. Finally, the transcription of nonlabeled images was requested to evaluate the algorithm. Our method raked second in this contest

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

On-the-fly Historical Handwritten Text Annotation

The performance of information retrieval algorithms depends upon the availability of ground truth labels annotated by experts. This is an important prerequisite, and difficulties arise when the annotated ground truth labels are incorrect or incomplete due to high levels of degradation. To address this problem, this paper presents a simple method to perform on-the-fly annotation of degraded historical handwritten text in ancient manuscripts. The proposed method aims at quick generation of ground truth and correction of inaccurate annotations such that the bounding box perfectly encapsulates the word, and contains no added noise from the background or surroundings. This method will potentially be of help to historians and researchers in generating and correcting word labels in a document dynamically. The effectiveness of the annotation method is empirically evaluated on an archival manuscript collection from well-known publicly available datasets

Recognizing Degraded Handwritten Characters

In this paper, Slavonic manuscripts from the 11th century written in Glagolitic script are investigated. State-of-the-art optical character recognition methods produce poor results for degraded handwritten document images. This is largely due to a lack of suitable results from basic pre-processing steps such as binarization and image segmentation. Therefore, a new, binarization-free approach will be presented that is independent of pre-processing deficiencies. It additionally incorporates local information in order to recognize also fragmented or faded characters. The proposed algorithm consists of two steps: character classification and character localization. Firstly scale invariant feature transform features are extracted and classified using support vector machines. On this basis interest points are clustered according to their spatial information. Then, characters are localized and eventually recognized by a weighted voting scheme of pre-classified local descriptors. Preliminary results show that the proposed system can handle highly degraded manuscript images with background noise, e.g. stains, tears, and faded characters

A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04346-8_58[EN] Paleography experts spend many hours transcribing historic documents, and state-of-the-art handwritten text recognition systems are not suitable for performing this task automatically. In this paper we present the modifications oil a previously developed interactive framework for transcription of handwritten text. This system, rather than full automation, aimed at assisting the user with the recognition-transcription process.This work has been supported by the EC (FEDER), the Spanish MEC under grant TIN2006-15694-C02-01 and the research programme Consolider Ingenio 2010 MIPRCV (CSD2007-00018) and by the UPV (FPI fellowship 2006-04).Romero Gómez, V.; Leiva Torres, LA.; Alabau Gonzalvo, V.; Toselli, AH.; Vidal Ruiz, E. (2009). A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images. En Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009, Corfu, Greece, September 27 - October 2, 2009. Proceedings. Springer Verlag (Germany). 459-460. https://doi.org/10.1007/978-3-642-04346-8_58S459460Toselli, A.H., et al.: Computer assisted transcription of handwritten text. In: Proc. of ICDAR 2007, pp. 944–948. IEEE Computer Society, Los Alamitos (2007)Romero, V., Toselli, A.H., Rodríguez, L., Vidal, E.: Computer assisted transcription for ancient text images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1182–1193. Springer, Heidelberg (2007)Toselli, A.H., et al.: Computer assisted transcription of text images and multimodal interaction. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 296–308. Springer, Heidelberg (2008)Romero, V.: et al.: Interactive multimodal transcription of text images using a web-based demo system. In: Proc. of the IUI, Florida, pp. 477–478 (2009)Romero, V., et al.: Improvements in the computer assisted transciption system of handwritten text images. In: Proc. of the PRIS 2008, pp. 103–112 (2008

Improvement of binarization performance using local otsu thresholding

Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises