Handwritten text recognition is challenging because of the virtually infinite ways a human can write the same message. Our fully convolutional handwriting model takes in a handwriting sample of unknown length and outputs an arbitrary stream of symbols. Our dual stream architecture uses both local and global context and mitigates the need for heavy preprocessing steps such as symbol alignment correction as well as complex post processing steps such as connectionist temporal classification, dictionary matching or language models. Using over 100 unique symbols, our model is agnostic to Latin-based languages, and is shown to be quite competitive with state of the art dictionary based methods on the popular IAM and RIMES datasets. When a dictionary is known, we further allow a probabilistic character error rate to correct errant word blocks. Finally, we introduce an attention based mechanism which can automatically target variants of handwriting, such as slant, stroke width, or noise.
@cite_24: Text recognition from natural scene images is very tough task now these days compare than videos. Application of image processing called pattern recognition make easy to recognize text from multimedia documents. A pattern can be fingerprint image, handwritten word sample, human face images, speech signal and DNA sequence etc or we can say that all pattern are in machine editable form. Text can be recognized with and without segmentation of character. Segmentation can be line, word or character level and without segmentation character is recognized from whole text image. Character recognition is a field of research and various research has been done in the area of pattern recognition. There we use a new technique called diagonal based feature extraction in last layer of convolutional neural network and make feature extraction easy with the help of genetic algorithm. After extraction of feature we provide training to extreme learning machine. Along this feature extraction technique we use feed forward network as a classifier and convolution neural network for feature extractor. It is a deep learning based technique of neural network which use for classification or recognition of text. This is basically used for providing training and in testing phase. CRConvNet has more layers working of all layer shown in flowchart. One dataset which contain 360 training set data that are all in capital(A-Z) and small(a-z) alphabet, digit (0–9) and some special character are also used. Another dataset contains samples of video and images (ICDAR 2003) for testing. Extensive studies shows that the recognition system which using diagonal based feature learning provide high recognition accuracy while requiring less time for training.
@cite_9: The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 × 3 and 5 × 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.
@cite_7: 
Recent works in scene text recognition @cite_24 @cite_9 use convolutional neural networks and pre-processing techniques like adjusting orientations to predict characters from a scene text. @cite_7 proposed an end-to-end framework of recognizing characters from a document by using LSTMs for character segmentation and CNNs for character classification.