Decision-Based Specification and Comparison of Table Recognition Algorithms

The vast majority of algorithms in the table recognition literature are specified informally as a sequence of operations [7]. This has the undesirable side effects that models of table structure are implicit, defined generatively by the sequence of operations, and that the effects of intermediate decisions are often lost as usually a single interpretation is modified in-place. We wished to compare the Handley [2] and Hu et al. [4]. table structure recognition algorithms and the complete set of table cell hypotheses they each generated, including any rejected in the final result. Rebuilding the systems using procedural code that transformed data structures for interpretations in-place would not have achieved this goal. Initially we translated the strategies to a formal model-based (specifically grammarbased) framework. A well designed model-driven system (such as DMOS by Couasnon ¨ [1]) makes it easier to observe and record decision making, and can be programmed succinctly by a model specification. However, we found mapping the sequence of operations in the strategies to a model based description was difficult, and the formal system required frequent and substantial reconfiguration in order to incorporate unanticipated requirements. We then considered an intermediate level of formalization. By using a small set of basic graph-based operations we could define recognition algorithms as a series of decisions, where the alternatives for each decision were model operations of a specified type (e.g. classifying table cells as header cells or data cells). This made the model operations considered and applied at each decision point explicit, permitted dependencies between logical types to be automatically recovered, and allowed the complete history of hypothesis creation, rejection, and reinstatement to be automatically captured. The resulting formalization is the Recognition Strategy Language (RSL)

Range Performance Modelling of Thermal Imaging System based on Single Parameter Characterised by Ambient Temperature and Relative Humidity

Range performance of a thermal imaging system is characterised by the prevailing atmospheric condition present at that time. There are two dominant parameters that limit the range performance of any thermal imaging systems i.e. ambient temperature and relative humidity. In the present work, comparative study of acquisition range performance of thermal imaging system operating in LWIR and MWIR spectral bands has been presented as a function of absolute humidity (AH) which is responsible for attenuation of IR radiation due to water vapour molecules present in path length. Presentation of acquisition range as function of AH leads to a single range performance table/graph for thermal imaging system under consideration for predefined visibility (V), target size, ambient temperature (T), target to background temperature difference (ΔT) and relative humidity (RH). This table/graph can be used to predict detection, recognition and identification ranges for any set of combination of air temperature (T) and relative humidity (RH). The approach presented in this paper is versatile and has been illustrated through comparative performance analysis of LWIR and MWIR thermal imaging systems based on 640X512 staring focal plane array (FPA) having identical design parameters in terms of resolution (IFOV). It has been shown that MWIR performance is superior to LWIR beyond a crossover value of AH(T) even though MRTD of MWIR sensor is inferior to that of LWIR sensor at all spatial frequencies. Study has been carried out both for clear atmosphere and hazy conditions

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.Comment: preprin