Abdellatif Ennaji

Text-independent writer recognition using multi-script handwritten texts

This paper presents a text-independent writer recognition method in a multi-script environment. Handwritten texts in Greek and English are considered in this study. The objective is to recognize the writer of a handwritten text in one script from the samples of the same writer in another script and hence validate the hypothesis that writing style of an individual remains constant across different scripts. Another interesting aspect of our study is the use of short handwritten texts which was implied to resemble the real life scenarios where the forensic experts, in general, find only short pieces of texts to identify a given writer. The proposed method is based on a set of run-length features which are compared with the well-known state-of-the-art features. Classification is carried out using K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM). The experimental results obtained on a database of 126 writers with 4 samples per writer show that the proposed scheme achieves interesting performances on writer identification and verification in a multi-script environment.

Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration

We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the HMM framework can significantly improve the discriminating capacity of the HMMs to deal with very difficult pattern recognition tasks such as unconstrained Arabic handwriting recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss, and Poisson) for the explicit state duration modeling have been used, and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.

HMMs with Explicit State Duration Applied to Handwritten Arabic Word Recognition

This paper describes an off-line segmentation-free handwritten Arabic words recognition system. The described system uses discrete HMMs with explicit state duration of various kinds (Gauss, Poisson and gamma) for the word classification purpose. After preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of feature vectors. Then, vector quantization is applied to this sequence and its output is submitted to a HMMs classifier based on a likelihood criterion for identifying the word using the viterbi algorithm. Several experiments were performed using the IFN/ENIT benchmark database, they showed, on the one hand, a substantial improvement in the recognition rate when HMMs with explicit state duration of either discrete or continuous distribution are used instead of classical HMMs (i.e. with implicit state duration), on the other hand, the gamma distribution for the state duration, that have given the best recognition rate (91.23 % in top 2), seems more suitable for the HMMs based modeling of Arabic handwriting

Script and nature differentiation for Arabic and Latin text images

A method for Arabic and Latin text block differentiation for printed and handwritten scripts is proposed. This method is based on a morphological analysis for each script at the text block level and a geometrical analysis at the line and the connected component level. In this paper, we present a brief survey, of existing methods used for scripts differentiation as well as a general characteristics of Arabic and Latin scripts. Then, We describe our method for the differentiation of these last scripts. We finally show two experimental results on two different data sets. 400 text blocks constitute the first one and 335 text blocks compose the second.

Writer recognition on arabic handwritten documents

Recognizing the writer of a handwritten document has been an active research area over the last few years and is at the heart of many applications in biometrics, forensics and historical document analysis. In this paper, we present a novel approach for text-independent writer recognition from Arabic handwritten documents. To characterize the handwriting styles of different writers involved in the evaluation of our approach, we have used two texture methods based on edge hinge features and run-lengths features. The efficiency of the proposed approach is demonstrated experimentally by the classification of 1375 handwritten documents collected from 275 different Arabic writers.

A Document Image Segmentation System Using Analysis of Connected Components

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper presents a method to separate the textual and non textual components in document images using a graph-based modeling and structural analysis. This is a fast and efficient method to separate adequately the graphical and the textual parts of a document. We have evaluated our method on two well-known subsets: the UW-III dataset and the ICDAR 2009 page segmentation competition dataset. Comparisons are led with two methods of state-of-the-art, these results showing that our method proved better performances in this task.

Linguistic integration information in the AABATAS Arabic text analysis system

An Arabic text analysis system called AABATAS (affixal approach-based Arabic text analysis system) is proposed. AABATAS recognizes and categorizes the words while identifying their morphological and grammatical characteristics. It is based on a new approach for Arabic word recognition called affixal approach. This affixal approach is guided by the structural properties of language. A dynamic decomposition-recognition mechanism is used in our system and leads to generate a set of reliable solutions for each word. This mechanism attempts to identify, the word basic morphemes: the prefix, the infix, the suffix and the root contrary to the existing approaches that are usually based on the recognition of the whole word or the pseudo-word or the letter. In this paper, we briefly present the general characteristics of Arabic texts as well as a succinct survey of the existing approaches used for their recognition. We then describe the structural properties of the Arabic language and the two systems based on these last properties. The first one concerns a word recognition process and the second is devoted to text analysis. We finally show two experimental results; one on a data set of 545 words and another on a text example.

Multi-script Writer Identification Optimized with Retrieval Mechanism

Identifying the writer of a handwritten document has been an active research area over the last few years with applications in biometrics, forensics, smart meeting rooms and historical document analysis. In this paper, we present a new writer identification system based on a retrieval mechanism. Texture based edge-hinge and run-length features are used to characterize the writing style of an individual. The effectiveness of the proposed system is evaluated on a total of 1583 writing samples in Arabic, German, English, French, and Greek from two different databases. The experimental evaluations reveal that reducing the search space using a writer retrieval mechanism prior to identification improves the identification rates.

A simple text/graphic separation method for document image segmentation

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper presents a method to separate the textual and non textual components in document images using a graph-based modeling and structural analysis. This is a fast and efficient method to separate adequately the graphical and the textual parts of a document. We have evaluated our method on two well-known subsets: the UW-III dataset and the ICDAR 2009 page segmentation competition dataset. Comparisons are led with two methods of state-of-the-art; these results showing that our method proved better performances in this task.

A methodology to spot words in historical Arabic documents

Libraries contain huge amounts of Arabic printed historical documents which cannot be available on-line because they do not have a searchable index. The word spotting idea has previously been suggested as a solution to create indexes for such a collection of documents by matching word images. In this paper we present a word spotting method for Arabic printed historical document. We start with word segmentation using run length smoothing algorithm. The description of the features selected to represent the words images is given afterwards. Elastic Dynamic Time Warping is used for matching the features of the two words. This method was tested on the Arabic historical printed document database of Moroccan National Library (MNL).