Laurence Likforman-Sulem

Text line segmentation of historical documents: a survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically.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

Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

In this paper, we describe a 1D HMM offline handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline-dependant features.

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

Combination of HMM-Based Classifiers for the Recognition of Arabic Handwritten Words

In this paper we present a two-stage system for the off-line recognition of cursive Arabic handwritten words. The proposed method is analytic without segmentation, and is able to cope with handwriting inclination and with shifted positions of diacritical marks. First, the recognition stage relies on 3 classifiers based on hidden Markov modelling (HMM). The second stage depends on the combination of these classifiers. The feature vectors used for recognition are related to pixel density distribution and to local pixel configurations. These vectors are extracted on word binary images by using a sliding window approach with different angles. We have experimented different combination schemes. The neural network-based combined system yields best performance on the IFN- ENIT benchmark data base of handwritten names of Tunisian villages/towns.

An expert vision system for analysis of Hebrew characters and authentication of manuscripts

Abstract This paper describes a knowledge based system that helps scribes to authenticate Hebrew manuscripts for which accurate laws of calligraphy have been given to the scribes. Paleographic expertise is also included in order to characterize the size of the document and the writing. When used for authentication purposes, the system shortens the task of the scribe by pointing out the parts of the document or the characters where problems arise for the machine (low contrast, breaks, ambiguous shape). The scribe will then operate on a restricted part of the document and decide whether it has to be corrected or not. In order to be close to human mechanisms of interpretation, the system structure includes several analysis levels. This system uses some principles of intelligent vision systems that can set up image and document analysis strategies as well as interactions between the high level and low level procedures. It works directly on grey level pictures.

An integrated reading and editing environment for scholarly research on literary works and their handwritten sources

We present an integrated system devoted to the visualization and the editing of hypermedia documents from literary material including document images and structured text. First, capabilities are offered to transcribe manuscript images. Transcribing the text consists in coupling lines typed on the keyboard with their corresponding text lines in the manuscript images. A semi-automatic system based on computer-human interaction and document analysis is proposed for performing this task. This system provides editing capabilities for linking document images and the corresponding structured textual representations (encoded by means of a logical markup language). Finally, application-specific visualization tools have been developed in order to provide users with an idea of the overall organization of the hyperdocument and help them to navigate.

Recognition of Arabic Handwritten Words using Contextual Character Models

In this paper we present a system for the off-line recognition of cursive Arabic handwritten words. This system in an enhanced version of our reference system presented in [El-Hajj et al., 05] which is based on Hidden Markov Models (HMMs) and uses a sliding window approach. The enhanced version proposed here uses contextual character models. This approach is motivated by the fact that the set of Arabic characters includes a lot of ascending and descending strokes which overlap with one or two neighboring characters. Additional character models are constructed according to characters in their left or right neighborhood. Our experiments on images of the benchmark IFN/ENIT database of handwritten villages/towns names show that using contextual character models improves recognition. For a lexicon of 306 name classes, accuracy is increased by 0.6% in absolute value which corresponds to a 7.8% reduction in error rate.

Recognition of Broken Characters from Historical Printed Books Using Dynamic Bayesian Networks

This paper investigates the application of dynamic Bayesian networks (DBNs) to the recognition of degraded characters from historical printed books. This framework allows us to capture the 2D nature of character images by the coupling of two HMMs (Hidden Markov Models). The vertical HMM observes image columns while the horizontal HMM observes image rows respectively. Two coupled DBN architectures are proposed to model interactions between these two streams. We present experiments on real degraded characters extracted from an ancient printed book (17th century). These experiments demonstrate that coupled architectures significantly better cope with broken characters than non coupled ones and than discriminative methods such as SVMs.

Proper names extraction from fax images combining textual and image features

In the frame of a unified messaging system, a crucial task of the system is to provide the user with key information on every message received, like keywords reflecting the object of the message, or the name of the sender. However, in the case of facsimiles, this information is not as easy to detect as in the case of e-mails, since no standard headers are defined. The aim of the presented work is to identify and extract specific information (the name of the sender) from a fax cover page. For this purpose, methods based on image document analysis (OCR recognition, physical blocks selection), and text analysis methods (optimized dictionary lookup, local grammar rules), are implemented to work in parallel. The fusion of their results brings a more accurate guess than any of the methods would achieve separately.

Image and text coupling for creating electronic books from manuscripts

Presents the first achievements of HERS (Hypermedia Edit and Read Station), which is devoted to the browsing and editing of hypermedia documents from literary material including document images. Concerning the editing, our purpose as twofold. First, capabilities are offered to transcribe manuscripts. Transcribing the text consists of coupling lines typed on the keyboard with their corresponding text lines in the manuscript images. A collaborative system, based on computer-human interaction and document analysis, is proposed for performing this task. Second, interactive tools are offered to organize the electronic document and establish hypermedia links between its different components (image areas, transcribed words or lines, or other kinds of heterogeneous data). Concerning the browsing, we developed an approach based on information visualization in order to provide users with an idea of the overall organization of the hyperdocument and so help them to navigate through it.