Copyeditors have to find out the errors in the manuscript and correct them manually. This process is taken up for automation using ML/DL techniques. This project is intended to find the grammatical and structural errors of the manuscript using various ML/DL techniques. Keras/Tensorflow is used to achieve the objective. This is third phase of the project and one  phase isalready live and second phase is in testing.

SMEs spend lot of time tagging the various attributes for products of our client. These tags are used in online searches on the client’s website. Deep Analysis using Tensorflow/Keras is used in analyzing images to extract attributes of the items. Our model first develops the tags for these products and then when a new item is introduced it would automatically tag the item.

Around 60% of the copyeditor’s time is used for checking the reference and give citation to the reference. This project aims at automatic restructuring of citation/reference and reduces the manual effort involved in the process. The project now increased the productivity of our copyeditors by around 50%.

Legal journals are to be maintained properly according to the classification for easy reference. There are around 28 categories in legal journals. The cases would have to be read and the keywords have to be found and using these keywords the categories are separated. The major challenge is to tag these journals to all the categories these cases fall. Kind of book keeping project very useful for legal practitioners.