Evaluations of Thinning Algorithms for Preprocessing of Handwritten Characters

Thinning algorithms have played an important role in preprocessing phase which decides the success of recognition in the OCR system. This paper report on the performance of 11 thinning algorithms from the perspective of character recognition where different aspects of the performance of each algorithm like computing time, deviation from perfect 8-connectedness, and number of possible noise spurs present in the skeletons are considered

Feedback Based Architecture for Reading Check Courtesy Amounts

In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either on intra-organization basis or on inter-organization basis, including paper intensive applications in the check processing application. In many countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. Such process of manual reading of each check involves significant time and cost. In this research, a new approach is introduced to read the numerical amount field on the check; also known as the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: segmentation, normalization, and the recognition of each character using a neural network classifier, with results better than many other methods in the literaratu

Handwritten Bank Check Recognition of Courtesy Amounts

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline's speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved Spatial Transformer Networks(STN). Meanwhile, a Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency

Informatisation d'une forme graphique des Langues des Signes : application au système d'écriture SignWriting

Les recherches et les logiciels présentés dans cette étude s'adressent à une importante minorité au sein de notre société, à savoir la communauté des sourdes. De nombreuses recherches démontrent que les sourdes se heurtent à de grosses difficultés avec la langue vocale, ce qui explique pourquoi la plu- part d'entre eux préfère communiquer dans la langue des signes. Du point de vue des sciences de l'information, les LS constituent un groupe de minorités linguistiques peu représentées dans l'univers du numérique. Et, de fait, les sourds sont les sujets les plus touchés par la fracture numérique. Cette étude veut donc être une contribution pour tenter de resserrer cette fracture numérique qui pénalise les sourdes. Pour ce faire, nous nous sommes principalement concentrés sur l'informatisation de SignWriting, qui constitue l'un des systèmes les plus prometteurs pour écrire la LS.The studies and the software presented in this work are addressed to a relevant minority of our society, namely deaf people. Many studies demonstrate that, for several reasons, deaf people experience significant difficulties in exploiting a Vocal Language (VL English, Chinese, etc.). In fact, many of them prefer to communicate using Sign Language (SL). As computer scientists, we observed that SLs are currently a set of underrepresented linguistic minorities in the digital world. As a matter of fact, deaf people are among those individuals which are mostly affected by the digital divide. This work is our contribution towards leveling the digital divide affecting deaf people. In particular, we focused on the computer handling of SignWriting, which is one of the most promising systems devised to write SLs