Bruno Taconet

Text line segmentation of historical documents: a survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically.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.

Text Line Segmentation of Historical Arabic Documents

This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips. The extracted text blocks obtained by horizontal projection are classified into three categories: small, average and large text blocks. After segmenting the large text blocks, the lines are obtained by matching adjacent blocks within two successive strips using spatial relationship. The document without overlapping or touching characters is segmented by making abstraction on the segmentation module of the large text blocks. The text line segmentation method has a 96% accuracy on a collection of 100 historical documents

A Statistical Method for an Automatic Detection of Form Types

In this paper, we present a method to classify forms by a statistical approach; the physical structure may vary from one writer to another. An automatic form segmentation is performed to extract the physical structure which is described by the main rectangular block set. During the form learning phase, a block matching is made inside each class; the number of occurrences of each block is counted, and statistical block attributes are computed. During the phase of identification, we solve the block instability by introducing a block penalty coefficient, which modifies the classical expression of Mahalanobis distance. A block penalty coefficient depends on the block occurrence probability. Experimental results, using the different form types, are given.

PRAAD: Preprocessing and Analysis Tool for Arabic Ancient Documents

This paper presents the new system PRAAD for preprocessing and analysis of Arabic historical documents. It is composed of two important parts: pre-processing and analysis of ancient documents. After digitization, the color or greyscale ancient documents images are distorted by the presence of strong background artefacts such as scan optical blur and noise, show-through and bleed-through effects and spots. In order to preserve and exploit this cultural heritage documents, we intend to create efficient tool that achieves restoration, binarisation, and analyses the document layout. The developed tool is done by adapting our expertise in document image processing of Arabic ancient documents, printed or manuscripts. The different functions of PRAAD system are tested on a set of Arabic ancient documents from the national library and the National Archives of Tunisia.

Classification of forms with handwritten fields by planar hidden Markov models

In this article, we present a method for modelling physical structure of forms with handwritten fields, by means of pseudo-bidimensional hidden Markov models (PHMMs). This description is then used for automatic classification of types of forms. With the nature of the document, which comprises handwritten fields, position and dimensions of significant rectangles are variable. Moreover, the phenomena of merging and fragmentation, induce an additional variability in the number of rectangles. They characterize the physical structure of a class of forms. Modelling by PHMMs is developed and appears as a suitable tool to solve the problems of the 2D random variability arising from automatic classification of forms.