Showing papers on "Handwriting recognition published in 2019"

Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective

Abstract: Neurodegenerative diseases, for instance Alzheimer's disease (AD) and Parkinson's disease (PD), affect the peripheral nervous system, where nerve cells send messages that control muscles in order to allow movements. Sick neurons cannot control muscles properly. Handwriting involves cognitive planning, coordination, and execution abilities. Significant changes in handwriting performance are a prominent feature of AD and PD. This paper addresses the most relevant results obtained in the field of online (dynamic) analysis of handwritten trials by AD and PD patients. The survey is made from a pattern recognition point of view, so that different phases are described. Data acquisition deals not only with the device, but also with the handwriting task. Feature extraction can deal with function and parameter features. The classification problem is also discussed along with results already obtained. This paper also highlights the most profitable research directions.

Track, Attend, and Parse (TAP): An End-to-End Framework for Online Handwritten Mathematical Expression Recognition

Abstract: In this paper, we introduce Track, Attend, and Parse (TAP), an end-to-end approach based on neural networks for online handwritten mathematical expression recognition (OHMER). The architecture of TAP consists of a tracker and a parser. The tracker employs a stack of bidirectional recurrent neural networks with gated recurrent units (GRU) to model the input handwritten traces, which can fully utilize the dynamic trajectory information in OHMER. Followed by the tracker, the parser adopts a GRU equipped with guided hybrid attention (GHA) to generate notations. The proposed GHA is composed of a coverage-based spatial attention, a temporal attention, and an attention guider. Moreover, we demonstrate the strong complementarity between offline information with static-image input and online information with ink-trajectory input by blending a fully convolutional networks-based watcher into TAP. Inherently, unlike traditional methods, this end-to-end framework does not require the explicit symbol segmentation and a predefined expression grammar for parsing. Validated on a benchmark published by the CROHME competition, the proposed approach outperforms the state-of-the-art methods and achieves the best reported results with an expression recognition accuracy of 61.16% on CROHME 2014 and 57.02% on CROHME 2016, using only official training dataset.

ICDAR 2019 CROHME + TFD: Competition on Recognition of Handwritten Mathematical Expressions and Typeset Formula Detection

Abstract: We summarize the tasks, protocol, and outcome for the 6th Competition on Recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare LATEX strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. The strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D Institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME - still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.

Writing in the Air with WiFi Signals for Virtual Reality Devices

Abstract: Recently, handwriting recognition approaches has been widely applied to Human-Computer Interface (HCI) applications. The emergence of the novel mobile terminals urges a more man-machine friendly interface mode. The previous air-writing recognition approaches have been accomplished by virtue of cameras and sensors. However, the vision based approaches are susceptible to the light condition and sensor based methods have disadvantages in deployment and highcost. The latest researches have demonstrated that the pervasive wireless signals can be used to identify different gestures. In this paper, we attempt to utilize channel state information (CSI) derived from wireless signals to realize the device-free air-write recognition called Wri-Fi . Compared to the gesture recognition, the increased diversity and complexity of characters of the alphabet make it challenging. The Principle Component Analysis (PCA) is used for denoising effectively and the energy indicator derived from the Fast Fourier Transform (FFT) is to detect action continuously. The unique CSI waveform caused by unique writing patterns of 26 letters serve as feature space. Finally, the Hidden Markov model (HMM) is used for character modeling and classification. We conduct experiments in our laboratory and get the average accuracy of the Wri-Fi are 86.75 and 88.74 percent in two writing areas, respectively.

Adversarial Generation of Handwritten Text Images Conditioned on Sequences

Abstract: We propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images yielding realistic-looking images on both French and Arabic languages. State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we could use those synthetic images to increase the amount of training data. We show that integrating generated images into the existing training data of a text recognition system can slightly enhance its performance.

Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning

Abstract: Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes. The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples; word-retrieval is therefore very difficult in low-resource scripts. Much of the existing literature comprises preprocessing strategies which are seldom sufficient to cover all possible variations. We propose an Adversarial Feature Deformation Module (AFDM) that learns ways to elastically warp extracted features in a scalable manner. The AFDM is inserted between intermediate layers and trained alternatively with the original framework, boosting its capability to better learn highly informative features rather than trivial ones. We test our meta-framework, which is built on top of popular word-spotting and word-recognition frameworks and enhanced by AFDM, not only on extensive Latin word datasets but also on sparser Indic scripts. We record results for varying sizes of training data, and observe that our enhanced network generalizes much better in the low-data regime; the overall word-error rates and mAP scores are observed to improve as well.

Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation

Abstract: This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

A Scalable Handwritten Text Recognition System

Abstract: Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building state-of-the-art models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTM-based models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.

Fast Multi-language LSTM-based Online Handwriting Recognition

Abstract: We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using Bezier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.

A Comparison of Three Classification Algorithms for Handwritten Digit Recognition

Abstract: Handwritten digits recognition is considered as a core to a diversity of emerging application. It is used widely by computer vision and machine learning researchers for performing practical applications such as computerized bank check numbers reading. However, executing a computerized system to carry out certain types of duties is not easy and it is a challenging matter. Recognizing the numeral handwriting of a person from another is a hard task because each individual has a unique handwriting way. The selection of the classifiers and the number of features play a vast role in achieving best possible accuracy of classification. This paper presents a comparison of three classification algorithms namely Naive Bayes (NB), Multilayer Perceptron (MLP) and K_Star algorithm based on correlation features selection (CFS) using NIST handwritten dataset. The objective of this comparison is to find out the best classifier among the three ones that can give an acceptable accuracy rate using a minimum number of selected features. The accuracy measurement parameters are used to assess the performance of each classifier individually, which are precision, recall and F-measure. The results show that K_Star algorithm gives better recognition rate than NB and MLPas it reached the accuracy of 82.36%.

A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition

Abstract: Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.

A Scalable Handwritten Text Recognition System

Abstract: Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building state-of-the-art models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTM-based models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.

Padding Methods in Convolutional Sequence Model: An Application in Japanese Handwriting Recognition

Abstract: Today, there is a wide range of research cases about end-to-end trained and sequence-to-sequence models applied in the task of handwritten character recognition. Most of which mark the combination between convolutional neural network (CNN) as a feature extraction module and recurrent neural network (RNN) as a sequence-to-sequence module. Notably, the CNN layer can be fed with dynamic sizes of input images while the RNN layer can tolerate dynamic lengths of input data, which subsequently makes up the dynamic feature of the recognition models. However, when the number one priority is to minimize the training timespan, the models are to receive training data in the form of mini-batch, which requires resizing or padding images into an equal size instead of using original multiple-size pictures due to the fact that most of the deep learning frameworks (such as keras, tensorflow, caffe, etc.) only accept the same-size input and output in one mini-batch. Actually, this practice may lower the model dynamicity in the training process. So, the question is whether it might be a trade-off between the effectiveness (level of accuracy) and the time optimization of the model. In this paper, we will examine different impact of various padding and non-padding methods on the same model architecture for Japanese handwriting recognition before finally concluding on which method has the most reasonable training time but can produce an accuracy rate of up to 95%.

SCUT-EPT: New Dataset and Benchmark for Offline Chinese Text Recognition in Examination Paper

Abstract: Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Abstract: Deep Convolutional Neural Network has recently gained popularity because of its improved performance over the typical machine learning algorithms. However, it has been very rarely used on recognition of Bangla handwritten digit. This paper proposes a Deep Convolutional Neural Network (DCNN) based Bangla handwritten digits recognition scheme. The proposed method applies a seven layered D-CNN containing three convolution layers, three average pool layers and one fully connected layer for recognizing Bangla handwritten digits. Rigorous experimentation on a relatively large Bangla digit dataset namely, CMATERdb 3.1.1 provides considerable recognition accuracies.

Cursive Handwritten Text Recognition using Bi-Directional LSTMs: A Case Study on Urdu Handwriting

Abstract: Recognition of cursive handwritten text is a complex problem due challenges like context sensitive character shapes, non-uniform inter and intra word spacings, complex positioning of dots and diacritics and very low inter class variation among certain classes. This paper presents an effective technique for recognition of cursive handwritten text using Urdu as a case study (though findings can be generalized to other cursive scripts as well). We present an analytical approach based on implicit character segmentation where convolutional neural networks (CNNs) are employed as feature extractors while classification is carried out using a bi-directional Long-Short-Term Memory (LSTM) network. The proposed technique is validated on a dataset of 6000 unique handwritten text lines reporting promising character recognition rates.

Segmentation-Free Bangla Offline Handwriting Recognition using Sequential Detection of Characters and Diacritics with a Faster R-CNN

Abstract: This paper presents an offline handwriting recognition system for Bangla script using sequential detection of characters and diacritics with a Faster R-CNN. This is an entirely segmentation-free approach where the characters and associated diacritics are detected separately with different networks named C-Net and D-Net. Both of these networks were prepared with transfer learning from VGG-16. The essay scripts from the Boise State Bangla Handwriting Dataset along with standard data augmentation techniques were used for training and testing. The F1 scores for the C-Net and D-Net networks are 89.6% and 93.2% respectively. Afterwards, both of these detection modules were fused into a word recognition unit with CER (Character Error Rate) of 11.2% and WER (Word Error Rate) of 24.4%. A spell checker further minimized the errors to 8.9% and 21.5% respectively. This same method is likely to be equally effective on several other Abugida scripts similar to Bangla.

Deep Network with Pixel-Level Rectification and Robust Training for Handwriting Recognition

Abstract: Offline handwriting recognition is a well-known challenging task in the optical character recognition (OCR) field due to the difficulty caused by various unconstraint handwriting styles. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN and RNN based model. We also propose an adjacent output mixup method for RNN layer's training to improve the generalization ability of the model, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model, and further analyze their contributions to the improvement of the model performance. The proposed method performs well on three public benchmarks, including the IAM, Rimes and IFN/ENIT datasets.

Hybrid DBLSTM-SVM Based Beta-Elliptic-CNN Models for Online Arabic Characters Recognition

Abstract: The deep learning-based approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid Beta-Elliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long short-term memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of feature-based systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and Online-KHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the on-line and off-line systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other state-of-the-art systems.

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances

Abstract: In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, application forms processing, postal address processing, to text-to-speech conversion. However, most research efforts are devoted to English-language only. This work focuses on developing Offline Arabic Handwriting Recognition (OAHR). The OAHR is a very challenging task due to some unique characteristics of the Arabic script such as cursive nature, ligatures, overlapping, and diacritical marks. In the recent literature, several effective Deep Learning (DL) approaches have been proposed to develop efficient AHWR systems. In this paper, we commission a survey on emerging AHWR technologies with some insight on OAHR background, challenges, opportunities, and future research trends.

Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition

Abstract: In this paper, we present a Seq2Seq model for online handwritten mathematical expression recognition (OHMER), which consists of two major parts: a residual bidirectional RNN (BiRNN) based encoder that takes handwritten traces as the input and a transition probability matrix introduced decoder that generates LaTeX notations. We employ residual connection in the BiRNN layers to improve feature extraction. Markovian transition probability matrix is introduced in decoder and long-term information can be used in each decoding step through joint probability. Furthermore, we analyze the impact of the novel encoder and transition probability matrix through several specific instances. Experimental results on the CROHME 2014 and CROHME 2016 competition tasks show that our model outperforms the previous state-of-the-art single model by only using the official training dataset.

Medical Handwritten Prescription Recognition Using CRNN

Abstract: Reading a doctor’s handwritten prescription is a challenge that most patients and some pharmacists face; an issue that, in some cases, lead to negative consequences due to wrong deciphering of the prescription. Part of the reason why doctor’s prescriptions are so difficult to decipher is that doctors make use of Latin abbreviations and medical terminology that most people don’t understand. This paper demonstrates how Artificial Neural Networks (ANN) is used to develop a system that can recognize handwritten English medical prescriptions. Using the Deep Convolution Recurrent Neural Network to train this supervised system, input images are segmented and processed to detect characters and classify them into the 64 different predefined characters. The results show that the proposed system yields good recognition rates and an accuracy of %98.

Ubiquitous Writer: Robust Text Input for Small Mobile Devices via Acoustic Sensing

Abstract: Efficient typing or text-input on mobile devices, such as smartphones and wearables is a long-standing problem, due to the miniature touchscreen on the devices. Recently, touchscreen-free solutions leveraging on acoustic sensing have been proposed, with the advantage of low cost and ubiquitous availability. However, existing solutions usually require people to write in print-style, and more importantly, they are highly vulnerable to environmental change, i.e., they need repetitive training upon slight deviation of writing places or device locations. Therefore, they are far from practical usage. In this paper, we propose a novel acoustic-based text-input system called UbiWriter , which can recognize freestyle handwriting with high ubiquity, i.e., one-time training and writing elsewhere. UbiWriter is built on a new letter recognition principle, which treats the acoustic signal from writing a letter as a complete trajectory, and then distills the recognition feature that is resilient to environmental change. For the actual realization of the principle, we adopt and incorporate a series of techniques, including a feature-preserved fast letter alignment, ${K}$ -nearest neighbor letter classification, and language structure-driven word recognition. We also design and implement an APP with cloud-computing support, in order to facilitate real-time text input. Extensive experimental results demonstrate that UbiWriter outperforms the state-of-the-art under various practical settings.

Offline Handwritten Malayalam Word Recognition Using a Deep Architecture

Abstract: Handwriting recognition is an important application of pattern recognition subject. Although some research studies of handwriting recognition of a few major Indian scripts can be found in the literature, the same is not true for many of the Indian scripts. Malayalam is one such script, and automatic recognition issues of this script remain largely unexplored till date. On the other hand, there are nearly 40 million people mainly living in the southern part of India whose native language is Malayalam. In the present article, we present our recent study of Malayalam offline handwritten word recognition. The main contributions of the present study are (a) pioneering development of a database for offline handwritten word samples of Malayalam script and (b) its benchmark recognition results based on a transfer learning strategy which involves a deep convolutional neural network (CNN) architecture for feature extraction and a support vector machine (SVM) for classification. Recognition result of the proposed architecture on the writer independent test set of Malayalam handwritten word sample database is quite satisfactory. Moreover, the same architecture has been found to improve the existing state of the art of offline handwriting recognition of several major Indian scripts.

Doctor’s Cursive Handwriting Recognition System Using Deep Learning

Abstract: Handwriting is a skill to express thoughts, ideas, and language. Over the years, medical doctors have been well-known for having illegible cursive handwritings and has been a generally accepted matter. The datasets used in this paper are samples of doctors cursive handwriting collected from several clinics and hospitals of Metro Manila, Quezon City and Taytay, Rizal. In this paper, we present the Handwriting Recognition System using Deep Convolutional Recurrent Neural Network that is developed in order to identify the text in the image of prescriptions written by the doctors and show the readable text conversion of the cursive handwriting. In this study two models were evaluated and based on the experimentation CRNN with model-based normalization scheme than the CRNN alone. This study achieved 76% training accuracy rate and the developed model was found successfully implemented in a mobile application, having achieved a validation accuracy of 72% for the validation set from the remaining 540 images of prescription. The mobile application was validated for the second time using the captured 48 handwriting samples written by the researchers and correctly identified 17 images out of 48 this gives us a 35% validation accuracy.

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Abstract: One of the most important steps in a handwriting recognition system is text-line and word segmentation. But, this step is made difficult by the differences in handwriting styles, problems of skewness, overlapping and touching of text and the fluctuations of text-lines. It is even more difficult for ancient and calligraphic writings, as in Arabic manuscripts, due to the cursive connection in Arabic text, the erroneous position of diacritic marks, the presence of ascending and descending letters, etc. In this work, we propose an effective segmentation of Arabic handwritten text into text-lines and words, using deep learning. For text-line segmentation, we used an RU-net which allows a pixel-wise classification to separate text-lines pixels from the background ones. For word segmentation, we resorted to the text-line transcription, as we have not got a ground truth at word level. A BLSTM-CTC (Bidirectional Long Short Term Memory followed by a Connectionist Temporal Classification) is then used to perform the mapping between the transcription and text-line image, avoiding the need of the input segmentation. A CNN (Convolutional Neural Network) precedes the BLST-CTC to extract the features and to feed the BLSTM with the essential of the text-line image. Tested on the standard KHATT Arabic database, the experimental results confirm a segmentation success rate of no less than 96.7% for text-lines and 80.1% for words.

Kannada Handwritten Script Recognition using Machine Learning Techniques

Abstract: Many researchers have contributed to automate the optical character recognition. But handwritten character recognition is still an uncompleted task. In this paper we are proposing two techniques to recognize handwritten Kannada script, which yields high accuracy compared to previous works. There are lot of challenges in recognizing handwritten Kannada scripts. Few of the challenges include: each person have their own handwriting, there is no uniform spacing between alphabets, words and lines. Another main problem when it comes to Kannada language is that there is no large dataset available to train the recognition system, and it is challenging to write all combinations of each alphabet in Kannada script. In the proposed work, we have gathered the handwritten training set from the Web and from the students of our campus and segmented each letter. We have proposed two methods to recognize the handwritten Kannada characters. The first techniques is by Tesseract tool, and second is by using Convolution Neural Network (CNN). With Tesseract tool we have achieved 86% accuracy and through Convolution Neural Network we achieved87% accuracy although it might improve with the data set chosen and further enhanced image processing. The main idea behind this work is to extract text from the scanned images, identify the Kannada letters in it accurately and display or store it for further usage.