Mobile edge computing (MEC) is important in location-based social networks (LBSNs). It puts services near users to cut delays. Edge service recommendation needs to mix context details with user privacy. Data sparsity makes this hard. Traditional methods have trouble with little data. They miss small context details or hurt privacy with central systems. This paper introduces FedCAFE, a federated learning system for edge service recommendation with context awareness. FedCAFE uses three main parts. It has a denoising autoencoder to get strong user and service features from small data. This tool learns patterns by fixing noisy information. It helps when user-service interactions are few. FedCAFE also uses a new adaptive fuzzy clustering method to group users and services by context matches. This part looks at things like time and place. It changes how it groups based on different situations. FedCAFE applies federated learning to keep privacy safe. It trains on user devices. It sends only model updates, not personal data. This stops private stuff like location from leaving the device. We tested FedCAFE on the WSDream dataset with real service information. FedCAFE beats other methods in these tests.

In this paper we present a two-level method to detect text in natural scene images. In the first level, connected components (referred as CCs) are got from the images. Then candidate text lines are extracted and groups of connected components that align in horizontal or vertical direction are got. We think CCs in these groups have high probability are texts. To validate which CC is text, a SVM is trained to make an initial decision. The output of SVM is calibrated to posterior probability. Then we use the information of posterior probability of SVM and information of whether the connected component is in a group to divide the connected components into four classes: texts, non-texts, probable texts and undetermined CCs. In the second level, a conditional random field model is used to make final decision. Relationship between CCs is modeled by a network G(V, E), Vertices of the graph correspond to CCs. The determination in the first level will influence the second levels determination by giving different parameters of data term for the four classes of CCs. By this way, we not only use information of a single CCs feature, but also use the information of whether a CC is in a group to make final decision of whether the CC is text or non-text. Experiments show that the method is effective.

This paper describes a model based framework for detection and extraction of the contents of table cells from degraded handwritten document images that contain tables. Given the very poor quality of the target documents, the table cell detection problem is formulated conceptually as a two-step process. The first step is to identify the location of the table and extract the content of table cells given a model of the structure of the table present in the image. The second step is to identify the model of the table present in a document image from a list of given table models. A model-based representation for tables is introduced and is used for matching table candidates with the given model to identify and extract the contents of table cells. The approach for detecting potential table candidates is based on the detection of horizontal and vertical table line candidates. The table representation is a matrix of horizontal and vertical table line crossings, and the matching algorithm is formulated as a minimization problem where the optimal table candidate is obtained using the minimal distance between the candidate and model table matrices which is then used for extraction of the table cell contents. A similar approach is used to solve the model selection problem where the best fitting location in the document page for each of the candidate models is identified using the distance minimization approach along with a confidence score and the model with the highest confidence score is selected as the correct model. The approach was tested on document page images containing tables from the challenge set of the DARPA MADCAT handwritten document image data. Results indicate that the method is effective for both model selection as well as table cell content extraction.

This paper presents a novel set of image enhancement algorithms for binary images of poorly scanned real world page documents. Problems that are targeted by the methods described include large blobs or clutter noise, salt-and-pepper noise and detection and removal of non-text objects such as form lines or rule-lines. The algorithms described are shown to be very effective in removing clutter noise and pepper noise as well as form lines and rule-lines. A region growing algorithm is also described to enhance the quality of the text and to fix the problems arising from the salt noise which leaves holes in the text and creates broken strokes. The methods were tested on 204 images from the challenge set of the DARPA MADCAT Arabic handwritten document image data. The results indicate that the methods described are robust and are capable of significantly improving the image quality for downstream OCR systems.

In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images.

We propose a new algorithm for segmentation of off-line handwritten Arabic words. The algorithm segments the connected letters to smaller segments each of which contains no more than three letters. Each letter may be segmented to at most five pieces. In addition to improving the recognition of Arabic words, another potential application of the proposed segmentation method is to build lexicon of small size, consisting of no more than three letter combinations. Generally, it is very hard to generate lexicon for recognition of unconstraint handwritten Arabic documents due to the large number of words of Arabic language.The algorithm has been tested on over 6300 words from 45 different documents written by 18 writers. The system is able to segment more than 93% of the words into segments, each containing at most one letter, 6% of the words into segments that contains two letters and 3% of the words into segments that contains three letters.

Partial Differential Equations are becoming one of the core tools for low-level image processing. They are especially functional in diffusion processes and variational models. In this paper, we exploit the regional smoothing that occurs in a nonlinear diffusion process and use this to enhance text in a degraded document image. The proposed smoothing method is robust when applied to either a highly corrupted text document or one with little degradation. The technique was tested on historical documents, carbon copies with highly varying grayscale backgrounds and on synthetic noisy documents. The PDE-based method far outperformed other industry-standard binarization techniques when compared quantitatively and qualitatively.

Page segmentation algorithms found in published literatures often rely on some predetermined parameters such as general font sizes, distances between text lines and document scan resolutions. Variations of these parameters in real document images greatly affect the performance of the algorithms. In this paper, we present a novel approach for document page segmentation using a multi-scale technique. An efficient implementation of a local connectivity algorithm transforms a document image into a parameter domain in which a parameter value at a pixel location represents a connectivity property for its neighboring foreground pixels in the original document image. Then a top-down approach with a linear search reveals the document regions at each scale levels as text block, text lines and graphics. We consider our algorithm a transform based multi-scale method. Our ongoing research shows that the algorithm is robust for variations of document parameters.

This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text lines and low quality image that do not lend themselves easily to binarizations. The algorithm is based on connectivity features similar to local projection profiles, which can be directly extracted from gray scale images. The proposed technique is robust and has been tested on a set of complex historical handwritten documents such as Newton's and Galileo's manuscripts. A preliminary testing shows a successful location rate of above 95% for the test set.

A format-driven word recognition system is proposed for recognition of handwritten words. Unlike most traditional handwritten word recognizers being given a set of target words as lexicon, we assume that our system is given a set of format descriptions other than lexicon words. Applications of the pro-posed system include recognition of relatively more important keywords such as postal codes, titles or trademarks. The for-mat descriptions are in terms of the lengths of the keywords, the types of the characters in the keywords and positional in-formations. Due to the important role of the keywords in the applications, the recognition expectations in terms of recogni-tion rate and accuracy are usually higher then lexicon-driven word recognizers. 1.

A skew angle estimation approach based on the application of a fuzzy directional runlength is proposed for complex address images. The proposed technique was tested on a variety of USPS parcel images including both machine print and handwritten addresses. The testing results showed a successful rate more than 90% of the test set.