{'fraction_non_alphanumeric': 0.016172506738544475, 'fraction_numerical': 0.0, 'mean_word_length': 5.243697478991597, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Handwriting Text Recognition using Neural Networks

Handwritten text recognition is a laborious task because humans can write a similar message in numerous ways or due to huge diversity in individual’s style of writing. The performance of text recognition systems implemented as neural networks has better results and accuracy than normal traditional classifiers. In this paper we explore the methods used to recognize and detect handwritten text or words in different languages. The major method used to recognize text is the Convolutional neural network (CNN) as a deep learning classifier. The other techniques used are Recurrent Neural Network (RNN) and a custom developed model called deep-writer, which is a variant of CNN architecture.