Courtesy Amount Recognition Research Articles

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition.

Courtesy amount recognition from bank checks is an important application of pattern recognition. Although much progress has been made on isolated digit recognition for Indian digits, there is no work reported in the literature on courtesy amount recognition for Arabic checks using Indian digits. Arabic check courtesy amount recognition comes with its own unique challenges that are not seen in isolated digit recognition tasks and, accordingly, need specific approaches to deal with them. This paper presents an end-to-end system for courtesy amount recognition starting from check images as input to recognizing amounts as a sequence of digits. The system is a hybrid system, combining rule-based modules as well as machine learning modules. For the amount recognition system, both segmentation-based and segmentation-free approaches were investigated and compared. We evaluated our system on the CENPARMI dataset of real bank checks in Arabic. We achieve 67.4% accuracy at the amount level and 87.15% accuracy at the digit level on the test set consisting of 626 check images. The results are presented with detailed analysis, and some possible future work is identified. This work can be used as a baseline to benchmark future research in Arabic check courtesy amount recognition.

HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios

Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications. Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision problems, especially in Handwritten Text Recognition (HTR). However, several approaches try to solve the problem of deep learning applied to Handwritten Digit String Recognition (HDSR), where it has to deal with the low number of trainable data, while learning to ignore any writing symbol around the digits (noise). In this context, we present a new optical model architecture (Gated-CNN-BGRU), based on HTR workflow, applied to HDSR. The International Conference on Frontiers of Handwriting Recognition (ICFHR) 2014 competition on HDSR were used as baselines to evaluate the effectiveness of our proposal, whose metrics, datasets and recognition methods were adopted for fair comparison. Furthermore, we also use a private dataset (Brazilian Bank Check - Courtesy Amount Recognition), and 11 different approaches from the state-of-the-art in HDSR, as well as 2 optical models from the state-of-the-art in HTR. Finally, the proposed optical model demonstrated robustness even with low data volume (126 trainable data, for example), surpassing the results of existing methods with an average precision of 96.50%, which is equivalent to an average percentage of improvement of 3.74 points compared to the state-of-the-art in HDSR. In addition, the result stands out in the competition’s CVL HDS set, where the proposed optical model achieved a precision of 93.54%, while the best result so far had been from Beijing group (from the competition itself), with 85.29%.

Automatic Bankcheck Processing: A New Engineered System

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system. Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system. Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

Bankcheck Recognition using Cross Validation Between Legal and Courtesy Amounts

A bankcheck reading system using cross validation of both the legal and the courtesy amounts is presented in this paper. Some of the challenges posed by the task are (i) segmentation of the legal amount into words, (ii) location of boundaries between dollars and cents amounts, and (iii) high accuracy in terms of recognition performance. Word segmentation in the legal amount is a serious issue because of the nature of the data and patrons' writing habits which tend to clump words together. We have developed a word segmentation algorithm based on the character segmentation results to address this issue. The list of possible amounts generated by the word segmentation hypotheses is used as lexicon for the courtesy amount recognition. The order of magnitude of the amount is estimated during legal amount recognition. We treat the courtesy amount as a numeral string and apply the same word recognition scheme as used for the legal amount. Our approach to check recognition differs from traditional methods in two significant aspects: First, our emphasis on both the legal and the courtesy amounts is balanced. We use an accurate word recognizer which performs equally well on alpha words and digit strings. Second, our combination strategy is serial rather than the commonly used parallel method. Experimental results show that 43.8% of check images are correctly read with an error rate of 0%.

Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields

Check amount recognition is one of the most promising commercial applications of handwriting recognition. This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks. Special attention is paid to a reliable procedure developed to reject doubtful answers. For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount. For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself are presented. We also present the explanation of problems existing at each stage and their possible solutions. The numeral recognizer used to read the amounts written in figures is described. This recognizer is based on the procedure of matching input subgraphs to graphs of symbol prototypes. Main principles of the handwriting recognizer used to read amounts written in words are explained. The recognizer is based on the idea of describing the handwriting with the most stable handwriting elements. The concept of the optimal confidence level of the recognition answer is introduced. It is shown that the conditional probability of the answer correctness is an optimal confidence level function. The algorithms of the optimal confidence level estimation for some special cases are described. The sophisticated algorithm of cross validation between legal and courtesy amount recognition results based on the optimal confidence level approach is proposed. Experimental results on real checks are presented. The recognition rate at 1% error rate is 67%. The recognition rate without reject is 85%. Significant improvement is achieved due to legal amount processing in spite of a relatively low recognition rate for this field.