Lucia Noce

Text Localization Based on Fast Feature Pyramids and Multi-Resolution Maximally Stable Extremal Regions

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach during both feature computation and connected component extraction allows our method to identify uncommon text elements that are usually not detected by competing algorithms, while the adoption of approximated features and appropriately filtered connected components assures a low overall computational complexity of the proposed system.

Interactive Object Class Segmentation for Mobile Devices

In this paper we propose an interactive approach for object class segmentation of natural images on touch-screen capable mobile devices. The key research question to which this paper tries to give an answer is: can we effectively correct the errors committed by an automatic or semi-automatic figure-ground segmentation algorithm while also providing real time feedback to the user on a low computational power mobile device? Many research works focused on improving automatic or semi-automatic figure-ground segmentation algorithms, but none tried to take advantage of the existing touch-screen technology integrated in most modern mobile devices to optimize the segmentation results of these algorithms. Our key idea is to use super-pixels as interactive buttons that can be quickly tapped by the user to be added or removed from an initial low quality segmentation mask, with the aim of correcting the segmentation errors and produce a satisfying final result. We performed an extensive analysis of the proposed approach by implementing it both on a desktop computer and a mid-range Android device, even though our method is extremely simple, the results we obtained are comparable with those achieved by other state-of-the-art interactive segmentation algorithms. As such, we believe that the proposed approach can be exploited by most image editing mobile applications to provide a simple but highly effective method for interactive object class segmentation.

Neural 1D Barcode Detection Using the Hough Transform

Barcode reading mobile applications to identify products from pictures acquired by mobile devices are widely used by customers from all over the world to perform online price comparisons or to access reviews written by other customers. Most of the currently available 1D barcode reading applications focus on effectively decoding barcodes and treat the underlying detection task as a side problem that needs to be solved using general purpose object detection methods. However, the majority of mobile devices do not meet the minimum working requirements of those complex general purpose object detection algorithms and most of the efficient specifically designed 1D barcode detection algorithms require user interaction to work properly. In this work, we present a novel method for 1D barcode detection in camera captured images, based on a supervised machine learning algorithm that identifies the characteristic visual patterns of 1D barcodes’ parallel bars in the two-dimensional Hough Transform space of the processed images. The method we propose is angle invariant, requires no user interaction and can be effectively executed on a mobile device; it achieves excellent results for two standard 1D barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the performance of a state-of-the-art 1D barcode reading library by coupling it with our detection method.

Robust Angle Invariant GAS Meter Reading

In this work we propose a novel method for automatic gas meter reading from real world images. In a wide range of countries all over the world, the existing automatic technology is not adopted, usually the reading is manually done on site, and a picture is taken through a mobile device as a proof of reading. In order to confirm the reading, a tedious work of checking the proof images is commonly done offline by an operator. With this contribution we aim to supply an effective system, able to provide a real support to the validation process reducing the human effort and the time consumed. We exploit both region-based and Maximally Stable Extremal Regions techniques, during the phase involving the localization of the meter area and to detect the meter counter digits in the detection step respectively. The evaluation has been carried out on every step of our approach, as well as on the overall assessment; although the problem is complex, the proposed method leads to good results even when applied to degraded images, it represents an effective solution to the gas meter reading problem and it can be utilized in real applications.

Automatic Prediction of Future Business Conditions

Predicting the future has been an aspiration of humans since the beginning of time. Today, predicting both macro- and micro-economic events is an important activity enabling better policy and the potential for profits. In this work, we present a novel method for automatically extracting forward-looking statement from a specific type of formal corporate documents called earning call transcripts. Our main objective is that of improving an analyst’s ability to accurately forecast future events of economic relevance, over and above the predictive contribution of quantitative firm data that companies are required to produce. By exploiting both Natural Language Processing and Machine Learning techniques, our approach is stronger and more reliable than the ones commonly used in literature and it is able to accurately classify forward-looking statements without requiring any user interaction nor extensive tuning.

Content Extraction from Marketing Flyers

The rise of online shopping has hurt physical retailers, which struggle to persuade customers to buy products in physical stores rather than online. Marketing flyers are a great mean to increase the visibility of physical retailers, but the unstructured offers appearing in those documents cannot be easily compared with similar online deals, making it hard for a customer to understand whether it is more convenient to order a product online or to buy it from the physical shop. In this work we tackle this problem, introducing a content extraction algorithm that automatically extracts structured data from flyers. Unlike competing approaches that mainly focus on textual content or simply analyze font type, color and text positioning, we propose novel and more advanced visual features that capture the properties of graphic elements typically used in marketing materials to attract the attention of readers towards specific deals, obtaining excellent results and a high language and genre independence.

Augmented text character proposals and convolutional neural networks for text spotting from scene images

In this work we propose a novel method for text spotting from scene images based on augmented Multi-resolution Maximally Stable Extremal Regions and Convolutional Neural Networks. The goal of this work is augmenting text character proposals to maximize their coverage rate over text elements in scene images, to obtain satisfying text detection rates without the need of using very deep architectures nor large amount of training data. Using simple and fast geometric transformations on multi-resolution proposals our system achieves good results for several challenging datasets while also being computationally efficient to train and test on a desktop computer.

Query and Product Suggestion for Price Comparison Search Engines based on Query-product Click-through Bipartite Graphs

Query suggestion is a technique for generating alternative queries to facilitate information seeking, and has become a needful feature that commercial search engines provide to web users. In this paper, we focus on query suggestion for price comparison search engines. In this specific domain, suggestions provided to web users need to be properly generated taking into account whether both the searched and the suggested products are still available for sale. To this end, we propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click through bipartite graph. Such graph is built using information extracted both from search engine logs and specific domain features such as categories and products popularities. Information collected from the query-product graph can be used to suggest not only related queries but also related products. The proposed model was tested on several challenging datasets, and also compared with a recent competing query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results both in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

A Query and Product Suggestion Method for Price Comparison Search Engines

In this paper we propose a query suggestion method for price comparison search engines. Query suggestion techniques are used for generating alternative queries to facilitate web users in information seeking; in this specific domain, suggestions provided to web users need to be properly generated taking into account that the suggested products must be still available for sale. We propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click-through bipartite graph. Information extracted both from search engine logs and specific domain features are exploited to build the graph, and one of the advantages of this model is that such a graph can be used to suggest not only related queries but also related products. Concepts used in the proposed method are not restricted to our context but are used in many other major e-commerce and search engine websites, we tested the model on several challenging datasets, and also compared with a recent query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

Using Convolutional Neural Networks for Content Extraction from Online Flyers

The rise of online shopping has hurt physical retailers, which struggle to persuade customers to buy products in physical stores rather than online. Marketing flyers are a great mean to increase the visibility of physical retailers, but the unstructured offers appearing in those documents cannot be easily compared with similar online deals, making it hard for a customer to understand whether it is more convenient to order a product online or to buy it from the physical shop. In this work we tackle this problem, introducing a content extraction algorithm that automatically extracts structured data from flyers. Unlike competing approaches that mainly focus on textual content or simply analyze font type, color and text positioning, we propose a new approach that uses Convolutional Neural Networks to classify words extracted from flyers typically used in marketing materials to attract the attention of readers towards specific deals. We obtained good results and a high language and genre independence.