Neighborhood Label Extension for Handwritten/Printed Text Separation in Arabic Documents

International audienceThis paper addresses the problem of handwritten and printed text separation in Arabic document images. The objective is to extract handwritten text from other parts of the document. This allows the application, in a second time, of a specialized processing on the extracted handwritten part or even on the printed one. Documents are first preprocessed in order to remove eventual noise and correct document orientation. Then, the document is segmented into pseudo-lines that are segmented in turn into pseudo-words. A local classification step, using a Gaussian kernel SVM, associates each pseudo-word into handwritten or printed classes. This label is then propagated in the pseudo-word's neighborhood in order to recover from classification errors. The proposed methodology has been tested on a set of public real Arabic documents achieving a separation rate of around 90%

On the Role of Context at Different Scales in Scene Parsing

Scene parsing can be formulated as a labeling problem where each visual data element, e.g., each pixel of an image or each 3D point in a point cloud, is assigned a semantic class label. One can approach this problem by training a classifier and predicting a class label for the data elements purely based on their local properties. This approach, however, does not take into account any kind of contextual information between different elements in the image or point cloud. For example, in an application where we are interested in labeling roadside objects, the fact that most of the utility poles are connected to some power wires can be very helpful in disambiguating them from other similar looking classes. Recurrence of certain class combinations can be also considered as a good contextual hint since they are very likely to co-occur again. These forms of high-level contextual information are often formulated using pairwise and higher-order Conditional Random Fields (CRFs). A CRF is a probabilistic graphical model that encodes the contextual relationships between the data elements in a scene. In this thesis, we study the potential of contextual information at different scales (ranges) in scene parsing problems. First, we propose a model that utilizes the local context of the scene via a pairwise CRF. Our model acquires contextual interactions between different classes by assessing their misclassification rates using only the local properties of data. In other words, no extra training is required for obtaining the class interaction information. Next, we expand the context field of view from a local range to a longer range, and make use of higher-order models to encode more complex contextual cues. More specifically, we introduce a new model to employ geometric higher-order terms in a CRF for semantic labeling of 3D point cloud data. Despite the potential of the above models at capturing the contextual cues in the scene, there are higher-level context cues that cannot be encoded via pairwise and higher-order CRFs. For instance, a vehicle is very unlikely to appear in a sea scene, or buildings are frequently observed in a street scene. Such information can be described using scene context and are modeled using global image descriptors. In particular, through an image retrieval procedure, we find images whose content is similar to that of the query image, and use them for scene parsing. Another problem of the above methods is that they rely on a computationally expensive training process for the classification using the local properties of data elements, which needs to be repeated every time the training data is modified. We address this issue by proposing a fast and efficient approach that exempts us from the cumbersome training task, by transferring the ground-truth information directly from the training data to the test data

Segmentation and labeling of documents using Conditional Random Fields

The paper describes the use of Conditional Random Fields(CRF) utilizing contextual information in automatically labeling extracted segments of scanned documents as Machine-print, Handwriting and Noise. The result of such a labeling can serve as an indexing step for a context-based image retrieval system or a bio-metric signature verification system. A simple region growing algorithm is first used to segment the document into a number of patches. A label for each such segmented patch is inferred using a CRF model. The model is flexible enough to include signatures as a type of handwriting and isolate it from machine-print and noise. The robustness of the model is due to the inherent nature of modeling neighboring spatial dependencies in the labels as well as the observed data using CRF. Maximum pseudo-likelihood estimates for the parameters of the CRF model are learnt using conjugate gradient descent. Inference of labels is done by computing the probability of the labels under the model with Gibbs sampling. Experimental results show that this approach provides for 95.75 % of the data being assigned correct labels. The CRF based model is shown to be superior to Neural Networks and Naive Bayes. Keywords: Conditional Random Field(CRF); labeling scanned documents; handwritten text extractio

Segmentation and labeling of documents using Conditional Random Fields

The paper describes the use of Conditional Random Fields(CRF) utilizing contextual information in automatically labeling extracted segments of scanned documents as Machine-print, Handwriting and Noise. The result of such a labeling can serve as an indexing step for a context-based image retrieval system or a bio-metric signature verification system. A simple region growing algorithm is first used to segment the document into a number of patches. A label for each such segmented patch is inferred using a CRF model. The model is flexible enough to include signatures as a type of handwriting and isolate it from machine-print and noise. The robustness of the model is due to the inherent nature of modeling neighboring spatial dependencies in the labels as well as the observed data using CRF. Maximum pseudo-likelihood estimates for the parameters of the CRF model are learnt using conjugate gradient descent. Inference of labels is done by computing the probability of the labels under the model with Gibbs sampling. Experimental results show that this approach provides for 95.75 % of the data being assigned correct labels. The CRF based model is shown to be superior to Neural Networks and Naive Bayes. Keywords: Conditional Random Field(CRF); labeling scanned documents; handwritten text extractio