Masakazu Iwamura

ICDAR 2013 Robust Reading Competition

This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.

Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval

Camera-based document image retrieval is a task of searching document images from the database based on query images captured using digital cameras. For this task, it is required to solve the problem of “perspective distortion” of images,as well as to establish a way of matching document images efficiently. To solve these problems we have proposed a method called Locally Likely Arrangement Hashing (LLAH) which is characterized by both the use of a perspective invariant to cope with the distortion and the efficiency: LLAH only requires O(N) time where N is the number of feature points that describe the query image. In this paper, we introduce into LLAH an affine invariant instead of the perspective invariant so as to improve its adjustability. Experimental results show that the use of the affine invariant enables us to improve either the accuracy from 96.2% to 97.8%, or the retrieval time from 112 msec./query to 75 msec./query by selecting parameters of processing.

ICDAR 2015 competition on Robust Reading

Results of the ICDAR 2015 Robust Reading Competition are presented. A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.

Real-Time Document Image Retrieval for a 10 Million Pages Database with a Memory Efficient and Stability Improved LLAH

This paper presents a real-time document image retrieval method for a large-scale database with Locally Likely Arrangement Hashing (LLAH). In general, when a database is scaled up, a large amount of memory is required and retrieval accuracy drops due to insufficient discrimination power of features. To solve these problems, we propose three improvements: memory reduction by sampling feature points, improvement of discrimination power by increasing the number of feature dimensions and stabilizing features by reducing redundancy. From the experimental results, we have confirmed that the proposed method realizes 50% memory reduction, and achieves 99.4% accuracy and 38ms processing time for a database of 10 million pages.

What is the Most EfficientWay to Select Nearest Neighbor Candidates for Fast Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) is a basic and important technique used in many tasks such as object recognition. It involves two processes: selecting nearest neighbor candidates and performing a brute-force search of these candidates. Only the former though has scope for improvement. In most existing methods, it approximates the space by quantization. It then calculates all the distances between the query and all the quantized values (e.g., clusters or bit sequences), and selects a fixed number of candidates close to the query. The performance of the method is evaluated based on accuracy as a function of the number of candidates. This evaluation seems rational but poses a serious problem; it ignores the computational cost of the process of selection. In this paper, we propose a new ANNS method that takes into account costs in the selection process. Whereas existing methods employ computationally expensive techniques such as comparative sort and heap, the proposed method does not. This realizes a significantly more efficient search. We have succeeded in reducing computation times by one-third compared with the state-of-theart on an experiment using 100 million SIFT features.

Real-Time Retrieval for Images of Documents in Various Languages Using a Web Camera

We propose a real-time retrieval method for document images in various languages. In this method, queries are images of documents captured by a web-camera. The document images corresponding to the queries are retrieved from the document image database in real time. Since we have already proposed a document image retrieval method for English documents, the proposed method is an extension for retrieval of documents in various languages. In the previous English document image retrieval method, only centroids of word regions are used as feature points. Therefore it cannot be applied to some languages including Japanese and Chinese due to no separation between words and periodic arrangements of characters. In the proposed method, additional features are introduced to realize real-time retrieval for document images in various languages.

Recognition of Multiple Characters in a Scene Image Using Arrangement of Local Features

Recognizing characters in a scene helps us obtain useful information. For the purpose, character recognition methods are required to recognize characters of various sizes, various rotation angles and complex layout on complex background. In this paper, we propose a character recognition method using local features having several desirable properties. The novelty of the proposed method is to take into account arrangement of local features so as to recognize multiple characters in an image unlike past methods. The effectiveness and possible improvement of the method are discussed.

Memory-based recognition of camera-captured characters

This paper addresses how to quickly recognize a character pattern using a lot of case examples without learning. Here without learning means just finding the most similar example from the case examples, and pretend as if the OCR understands the definition of the character. This strategy is expected to work well in most cases with a large dataset, however, also expected to take a lot of time for finding the most similar example. In this paper, we show that a lot of case examples can be processed in a short time. As a testbed, we handle recognition problem of camera-captured printed characters. Using a database storing 100 fonts, the proposed method achieved 97.0% of recognition rate for images captured from the right angle and 95.8% for those from 45 deg. with 4.56ms of processing time, that is about 220 characters per second including every process.

Real-Time Document Image Retrieval on a Smartphone

This paper presents a novel interface running on smart phones which is capable of seamlessly linking physical and digital worlds through paper documents. This interface is based on a real-time document image retrieval method called Locally Likely Arrangement Hashing. By just only pointing a smart phone to a paper document, the user can obtain its corresponding electronic document. This can easily provide the user with the information associated with the retrieved document. This relevant information can be superimposed on the display of smart phones. Therefore, we consider that with the help of this interface, the user can utilize paper documents as a new medium to display various information.

Improvement of Retrieval Speed and Required Amount of Memory for Geometric Hashing by Combining Local Invariants

Thegeometrichashing(GH) is a well-knownmodel-basedobject recognition techniquewith goodpropertiesbothin retrievalspeedandrequiredamountof memory. However, it has a significant weak point; as the number of objects increases, both retrieval speed and required amount of memory increase in the cubic, fourth or higher order. Recently, a new technique “locally likely arrangement hashing (LLAH)” whose computational cost is a linear order has been proposed. The objective of the current paper is to reveal how LLAH improves the performance. By comparing GH and LLAH, we describe four primary factors of the performance improvement.